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Mobile backhaul performance and the challenge of 5G, convergent technologies

25 Jan

InfoVista looks at mobile backhaul performance challenges

Mobile backhaul is already a massive challenge for mobile network operators today, as the demand for coverage and capacity that can handle data has exploded at a breakneck pace over the past several years. Assuring quality is only poised to become even more difficult with the onset of new convergent technologies and the brewing-storm that is widespread “5G” adoption on the horizon.

Mobile traffic trends

According to the June 2015 Ericsson Mobility Traffic Report, LTE mobile subscribers will show a growth of 40% compound annual growth rate from 2014 to 2020. It’s expected 3G networks will still be the dominant access technology by 2020 – and 2G will still be common in many developing regions – but new connectivity networks will also be coming online in the next few years that will put greater pressure on the already pressed global market.

The gap between traffic and revenue

The problem with this proliferation of data usage in relation to backhaul is the financial pressures that are already weighing heavily upon network provider capabilities. Despite accounting for almost 85% of network usage, data accounts for only 40% of network revenue.

An even more startling picture of the gap between traffic capacity and revenue comes to light when considering from 2008 to 2013, data traffic grew 46-times over while revenue from data over the same period only saw a three-fold increase.

The fact LTE networks natively don’t carry voice traffic is a contributing factor to the challenges network operators face when dealing with this traffic explosion. Providers need to find a way to share investments being made into LTE backhaul with voice (circuit-switched) services, while making sure this reallocation can still assure quality of service in a convergent scenario.

The real test for network providers is going to come with the arrival of 5G. The challenge stems from the fact not only will more customers the world over demand data-centric plans, but the bandwidth needs are on track to balloon well beyond current network capabilities. Research has indicated peak bandwidth per device may reach up to 1 gigabit per second on average, which is much greater magnitude than what LTE networks can deliver today.

If that was not enough, new 5G use cases such as “tactile Internet” will require extreme real-time communications, demanding backhaul networks deliver end-to-end latency as low as 5 milliseconds, which is an order of magnitude less than what the best LTE networks can deliver today.

What it means for tomorrow’s backhaul networks

It’s not as if existing “legacy” networks can be simply replaced or deactivated. In fact, mobile operators face a scenario where network complexity will only increase in the long run, as next-generation LTE and 5G access networks are coming down the pipeline. That, in turn, means legacy networks will co-exist with new technologies for the foreseeable future, adding yet another dimension to network operations that engineering teams need to handle when operating and assuring the quality of the network.

In fact, because of all these factors combined, mobile backhaul operations are becoming larger and less predictable to manage with traditional tools. In the past, 2G networks were predominantly voice-oriented and deployed on top of traditional, extremely reliable TDM/SDH backhauls. In 3G, we often see hybrid deployments where TDM/SDH co-exist with IP/MPLS, ATM and even Ethernet-based backhauls.

The arrival of LTE, LTE-Advanced and newer technologies such as voice over LTE heralded some more drastic changes to the way operators approached backhaul. Many mobile operators decided to migrate the entire backhaul to fully convergent technologies such as IP/MPLS and carrier Ethernet, effectively transporting all voice and data traffic on top of packet-switched networks.

With a larger and less predictable backhaul to manage, “up” or “down” indicators became evidently insufficient. The very fact different backhaul domains (access, aggregation, metro) and even different backhaul network layers could greatly affect each other’s performance – as well as the overall quality of service parameters – meant monitoring and troubleshooting the mobile backhaul with multiple disconnected tools became impractical.

The perspective: if the mobile backhaul already looks complex today – being composed of a litany of vendors, technologies and topologies – it will become even more complex, with both real and virtual networks devices poised to coexist in 5G software-defined networking and network functions virtualization native architectures – not to mention new network architectures will need to co-exist with the legacy for a long time. After all, subscribers by 2020 will still rely on 2G and 3G to access for both data and voice services.

How to deliver on future mobile backhaul expectations

From whatever angle you look at it, mobile backhaul is becoming larger and more complex to manage in the coming years. Yet, as daunting as this forecast for the next few years may seem for mobile operators, there are tools available that will help mobile operators support this increasing complexity while maintaining exceptional QoS.

Real-time, multi-layer troubleshooting – the ability to monitor all layers of the mobile backhaul in real-time – will play a key role in managing voice and data quality of experience in the new mobile landscape, reducing time to repair and increasing network uptime.

Providers will also need to rely on automated network topology discovery. A modern performance management tool must automatically handle these changes, so that the network operations center and software operations center can actually focus on monitoring the network, rather than expending time and energy manually cross checking and correcting grouped KPIs.

Equally important is to have end-to-end cross-domain visibility, where operation teams can see the performance of the radio access network, backhaul and core in one single plane of glass. This is also important to enable different teams (ex: RAN and transport) to work together and accelerate the time to resolution of these more complex cross-domain scenarios.

And with network capacity expectations on pace to boom, providers need to always be ahead of the game and have a plan for future fluxes in capacity. A performance management tool will need to monitor traffic KPIs evolutions and trends, extrapolate historical data and act proactively to adjust (right-size) backhaul links as they see fit.

Ultimately, a unified performance management tool can help operators increase the quality of experience of mobile subscribers, resulting in less churn and revenue protection. It also brings a series of other capital expense and operating expense gains, with a reduction in the number of tools and their associated costs.

In fact, these business benefits can be quantified and measured, and past experience has shown even more complex performance assurance consolidation projects can pay for themselves (return on investment) in 12 to 24 months (depending on the case).


Some mobile operators are reluctant to make the investment, in part thinking traditional assurance practices and tools they have used up until today can handle the job. But as we discussed, the network size and complexity, as well its statistical behavior, will demand the adoption of modern unified performance management tools.

If that was not enough, there are clear business benefits in doing so, resulting in a clear ROI for the investment. Even for those mobile operators with capex restrictions, there are windows of opportunity to make this necessary move, especially in view of new cloud-based performance management solutions that allow operators to switch to an opex-based model and expedite the ROI even further.

Those mobile operators that act decisively will prosper; those that hesitate are likely to find themselves playing catch up.

Editor’s Note: In an attempt to broaden our interaction with our readers we have created this Reader Forum for those with something meaningful to say to the wireless industry. We want to keep this as open as possible, but we maintain some editorial control to keep it free of commercials or attacks. 


Small cells: the only way to 5G

11 Nov

Workable 5G needs small cells, the author argues

It’s been estimated that the volume of global monthly mobile data traffic will exceed 15 exabytes by 2018. LTE is already proving to be a major bandwidth hog. While 4G represents only a fraction of mobile connections today, it accounts for at least 30% of mobile data traffic, thanks to a surge in high-bandwidth content such as video calling and music streaming.

Yet, the growth in bandwidth demand is not only about smartphones, tablets and other mobile computing gadgets. The sales of these devices are set to reach 2.4 billion units this year, but other types of connected ‘things’ will require their share of the already stretched networks too. Industry analysts have estimated that the number of wireless connected things will exceed 16 billion in 2014, up 20% from the year before. This growth is set to continue as the Internet of Things gathers pace, with more than double the number of connected devices – 40.9 billion – forecasted for 2020.

As existing 3G and 4G networks struggle to cope with the influx in data traffic, mobile operators are looking at solutions to offload traffic from their current base station networks. Small cells will be their solution of choice – so the number of small cells networks deployed across Europe is going to increase dramatically over the next few years. Small cells that are connected to city-wide superfast fibre networks will be the most economic and scalable way of ensuring that the needs of mobile users for more and more bandwidth are met in the future. Small cells will also be an enabler for the Internet of Things, paving the way for more connections than ever before.

Shortcomings of rooftop base stations

Today’s badly congested 3G and 4G networks rely on rooftop base stations. Many operators have been scrambling to acquire enough rooftop space for LTE, but still 4G networks don’t often meet their bandwidth hungry customers’ expectations, especially in dense urban areas such as pedestrian zones. While filling rooftops with base stations might have been a good solution for 3G, in the LTE era, the cells are becoming smaller, and mobile operators need ten times more base stations to cover the same footprint of a city.

Imagine a situation today where you have five people waiting for a bus, all with a brand new 150 mbps iPhone 6. The existing rooftop base station infrastructure is not able to cope with the sudden surge in bandwidth demand, as all five try to read the news, order groceries or download a restaurant menu, at the same time.

Recognising the need for faster evolution of mobile networks, the European Commission has committed to investing up to €700 million for the developments of ‘ubiquitous 5G communication systems’. This funding is part of a joint public and private sector initiative that aims to overcome today’s data traffic challenges. The ambitious goals of this 5G initiative include increasing wireless area capacity by a factor of 1,000 compared to 2010, creating a high-bandwidth network with 0% downtime, and enabling the roll-out of very dense wireless networks that are able to connect over 7 trillion devices amongst 7 billion people.

Getting ready for the future

As mobile operators gear themselves up for 5G, many of them realise that they can no longer rely on rooftop base stations. Why would a customer splurge on a 5G contract and a 5G-ready smartphone, if they aren’t able to get superfast download speeds? Instead, they will go to an operator that is able to give them the capacity they crave.

To eliminate the well-known capacity problems with rooftop base stations, future proof their networks and stay competitive, more and more European mobile operators are starting to tap into small cells. They are realising only small cells connected to fibre can bring mobile users the great user experience they expect on their LTE-enabled superfast mobile devices – down at street level where it really matters. When connected to fibre networks, these small cells can collectively deliver up to Gigabytes per second of capacity, making entire cities 5G ready in a cost effective way.

The mobile operator community has been talking about the potential of small cells for a couple of years, but up until recently, the size of the boxes prevented their widespread use. All leading networking vendors have invested in the development of more suitable equipment, so the technology is now ready to allow mobile operators to start planning their roll-outs in earnest.

To be able to roll out faster than their rivals, many European mobile operators are now starting to buy space on lampposts, billboards, bus stops or even public toilets, and equip them with  small cells.

Small cells – the only way to 5G

Still in recovery from the substantial investment needed for 4G, some cost-conscious mobile operators might be tempted to tighten the purse strings with small cells to protect their margins.

Yet, they really don’t have a choice but to invest. If they don’t, they will lose customers. It’s as simple as that. Why would a user buy a top of the range LTE-enabled smartphone or smartwatch, if they aren’t able to make the most of its superfast download speeds – unless they are standing on a rooftop? Instead, they will get their device from an operator that is able to give them the capacity they crave.

Other small cells-ready players aren’t the only competitive threat for mobile operators. Street furniture providers might eat into the profits of those mobile operators who drag their heels over small cells too. Through city-wide wifi schemes, street furniture companies are eliminating completely the need for mobile users to use their operator for data in some cases. Why would a mobile user pay a premium for patchy 5G connectivity, if they can get better speeds and coverage with free wifi?

In any way you look at it, 5G will only materialise with small cells connected to existing superfast fibre networks. And all European mobile operators’ competitiveness – and survival – will rely on 5G.



SK Telecom’s Network Evolution Strategies: Carrier aggregation, inter-cell coordination and C-RAN architecture

8 Oct

SK Telecom is the #1 mobile operator in Korea, with sales of KRW 16.6 trillion (USD 15.3 billion) in 2013, and with 50.1% of a mobile mobile subscription market share in 2Q 2014. It launched LTE service back in July 2011, and now more than half of its subscribers are LTE service subscribers, with 55.8% of LTE penetration as of 2Q 2014.
Due to LTE subscription growth, more advanced device features, and high-capacity contents, LTE networks are experiencing an unprecedented surge in traffic. To accommodate the flooded traffic, SK Telecom adopted LTE-A (Carrier Aggregation, CA) in 2013, and Wideband LTE-A (Wideband CA) in 2014 for improved network capacity.
As another effort to increase network capacity, the company made LTE/LTE-A macro cells a lot smaller, as small as hundreds of meters long, resulting in an increased number of cell sites. To save costs of building and operating the increased number of cell sites, it has built C-RAN (Advanced-Smart Cloud Access Network, A-SCAN, as called by SK Telecom) through BBU concentration since January 2012.
In 2014, SK Telecom began to introduce small cells (low-power small RRHs) in selected areas. As with macro cells, small RRHs have the same C-RAN architecture where they are connected to concentrated BBU pools through CPRI interfaces. SK Telecom calls it “Unified RAN (Cloud and Heterogeneous)”.
To prevent performance degradation at cell edges caused by introduction of small cells, SK Telecom developed HetNet architecture (known as SUPER Cell) where macro cells cooperate with small cells. The company, aiming to commercialize 5G networks in 2020, plans to commercialize SUPER Cell first in 2016, as a transitional phase to 5G networks.



Figure 1. SK Telecom’s Network Evolution Strategies
We analyzed SK Telecom’s network evolution strategies using the following three axes: 1) Carrier Aggregation (CA), 2) Inter-Cell Coordination, and 3) RAN Architecture in the Figure 1. Here, the CA axis shows how speeds have been and can be increased (n times) by expanding total frequency bandwidth aggregated. The Inter-Cell Coordination axis displays the company’s strategy to achieve higher speeds at cell edges by improving frequency efficiency. Finally, the RAN Architecture axis shows SK Telecom’s plan to switch to an architecture that would yield better LTE-A performance at reduced costs of building and operating RAN. Figure 2 is SK Telecom’s evolved LTE-A network, as illustrated according to the evolution strategies shown in Figure1.



Figure 2. SK Telecom’s LTE-A Evolution Network 



1. CA Evolution Strategies
CA is a technology that combines up to five frequencies in different bands to be used as one wideband frequency. It allows for expanded radio transmission bandwidth, which would naturally boost transmission speeds as much as the bandwidth is expanded. So, for example, if bandwidth is increased n times, then so is the transmission speed. Table 1 shows the LTE frequencies that SK Telecom has as of September 2014, totaling 40 MHz (DL only) across three frequency bands, which operate as Frequency Division Duplexing (FDD).
SK Telecom commercialized CA in June 2013 for the first time in the world, and then Wideband CA a year later in June 2014. 


It is now offering a maximum speed of 225 Mbps through the total 30 MHz bandwidth. As of May 2014, out of the total 15 million LTE subscribers, 3.5 million (23%) subscribers are using CA-enabled devices. Let’s see where SK Telecom’s CA is heading.


1.1 Combining More Bands: 3-band CA
3-band CA combines three frequency bands, instead of the current two, for wider-band transmission. Currently, SK Telecom has three LTE frequency bands, and is offering 2-band CA of 20 MHz or 30 MHz by combining two of the bands at once. This is because, although LTE-A standards technically support combining of up to five frequency bands, RF chips in  CA-enabled mobile devices available now can support combining of two bands only.  
3-band LTE devices are on the way and will be arriving in the market soon – sometime in early 2015 or by late 2014 at the latest. So, SK Telecom is planning to commercialize 3-band CA that combines all of its three frequency bands, just in time. The commercialization of 3-band CA is expected to increase transmission bandwidth to 40 MHz and data transmission rate to 300 Mbps. SK Telecom is also planning to combine three 20 MHz bands to further expand transmission bandwidth up to 60 MHz, and boost data transmission rate to 450 Mbps.


1.2 Femto Cell with CA
SK Telecom commercialized LTE Femto cell for the first time in the world in June 2012, to provide indoor users with more stable communication quality, and now is attempting to apply CA technology to Femto cell as well. The company completed a technical demonstration of LTE-A Femto cell in MWC 2014, proving it is capable to support 2-band CA. It will be conducting trial tests in a commercial network in late 2014 for final commercialization of the technology in 2015.


1.3 Combining Heterogeneous Networks: LTE-Wi-Fi CA
In July 2014, SK Telecom performed a technical demonstration of heterogeneous CA that combines LTE and Wi-Fi bands by using multipath TCP (MPTCP), an IETF standard. MPTCP is designed to combine more than one TCP flow (or MPTCP subflow) to make a single MPTCP connection, and send data through it. This technology is applied to a device and application server. In the demonstration, an MPTCP proxy server was used instead of an application server (Figure 3).    


Figure 3. LTE – Wi-Fi CA using Multipath TCP (MPTCP)
This technology will allow SK Telecom to combine i) its LTE bands that are currently featuring 2-band CA and ii) 802.11ac-based Giga Wi-Fi bands, together offering up to 1 Gbps or so. 
The detailed commercialization timeline is to be determined in accordance with the company’s plan for future development of MPTCP device and server.


1.4 Combining Heterogeneous LTE Technologies: FDD-TDD CA
This method enables operators to expand transmission bandwidth by combining two different types of LTE technologies: FDD-LTE and TDD-LTE. In a demonstration performed in Mobile Asia Expo in June 2014, SK Telecom successfully demonstrated FDD-TDD CA using ten 20 MHz bandwidths and 8×8 MIMO antenna showing 3.8 Gbps throughout. 


Cisco: The U.S. officially enters the gigabyte era of mobile data consumption

6 Feb
mobile data traffic world

SUMMARY:Average mobile data use in North America nearly doubled in 2013 to 1.38 GBs a month leading the world. The U.S. isn’t the biggest data hog — that would be Japan — but LTE is driving consumption.

Mobile users the world over came close to doubling their mobile data consumption between 2012 and 2013 as average monthly usage peaked well over 1 GB in the U.S, and several other countries, according to Cisco Systems’ new Visual Networking Index report on global mobile data trends.

North America led the pack with the average mobile subscriber consuming 1.38 GBs a month, up from 752 MBs in 2012 and a full gigabyte more than the average global usage of 356 MBs. When looking at individual countries, Japanese users led the world with 1.87 GBs, followed by the U.S. at 1.41 GBs and South Korea with 1.25 GBs.

Cisco VNI 2014 data use

Those three countries happen to be the first to launch LTE networks on a large scale, and according to Cisco director of service provider marketing Thomas Barnett, 4G adoption has become the strongest indicator of skyrocketing mobile broadband use worldwide, a conclusion other studies have also reached. In Western Europe, where LTE only got off the ground recently, average mobile data usage is half that in North America, coming in at 717 MBs per month.

Cisco VNI 2014 connection type

As you might expect, smartphones are a big driver of that increased data appetite, but tablets are contributing as well. Cisco, however, is seeing a surprising surge in PC connections to mobile networks. Barnett attributes that to the fact that laptops are starting to resemble tablets, coming with touchscreen capabilities and retractable keyboards. As people start to use their laptops like tablets, they’re increasing treating them as mobile — not merely portable — devices.

Cisco VNI 2014 devices

Last year the world saw 1.5 exabytes — an exabyte being 1 billion gigabytes — of data traverse its mobile networks each month. Cisco expects that number to grow to 15.9 exabytes in 2018.

Cisco VNI 2014 Traffic


Cisco Visual Networking Index: Global Mobile Data Traffic Forecast Update, 2013–2018

6 Feb



The Cisco® Visual Networking Index (VNI) Global Mobile Data Traffic Forecast Update is part of the comprehensive Cisco VNI Forecast, an ongoing initiative to track and forecast the impact of visual networking applications on global networks. This paper presents some of Cisco’s major global mobile data traffic projections and growth trends.

Executive Summary

The Mobile Network in 2013

Global mobile data traffic grew 81 percent in 2013. Global mobile data traffic reached 1.5 exabytes per month at the end of 2013, up from 820 petabytes per month at the end of 2012.

Last year’s mobile data traffic was nearly 18 times the size of the entire global Internet in 2000. One exabyte of traffic traversed the global Internet in 2000, and in 2013 mobile networks carried nearly 18 exabytes of traffic.

Mobile video traffic exceeded 50 percent for the first time in 2012. Mobile video traffic was 53 percent of traffic by the end of 2013.

Over half a billion (526 million) mobile devices and connections were added in 2013. Global mobile devices and connections in 2013 grew to 7 billion, up from 6.5 billion in 2012. Smartphones accounted for 77 percent of that growth, with 406 million net additions in 2013.

Globally, smart devices represented 21 percent of the total mobile devices and connections in 2013, they accounted for 88 percent of the mobile data traffic. In 2013, on an average, a smart device generated 29 times more traffic than a non-smart device.

Mobile network connection speeds more than doubled in 2013. Globally, the average mobile network downstream speed in 2013 was 1,387 kilobits per second (Kbps), up from 526 Kbps in 2012.

In 2013, a fourth-generation (4G) connection generated 14.5 times more traffic on average than a non-4G connection. Although 4G connections represent only 2.9 percent of mobile connections today, they already account for 30 percent of mobile data traffic.

The top 1 percent of mobile data subscribers generated 10 percent of mobile data traffic, down from 52 percent at the beginning of 2010. According to a mobile data usage study conducted by Cisco, mobile data traffic has evened out over the last year and is now lower than the 1:20 ratio that has been true of fixed networks for several years.

Average smartphone usage grew 50 percent in 2013. The average amount of traffic per smartphone in 2013 was 529 MB per month, up from 353 MB per month in 2012.

Smartphones represented only 27 percent of total global handsets in use in 2013, but represented 95 percent of total global handset traffic. In 2013, the typical smartphone generated 48 times more mobile data traffic (529 MB per month) than the typical basic-feature cell phone (which generated only 11 MB per month of mobile data traffic).

Globally, there were nearly 22 million wearable devices (a sub-segment of M2M category) in 2013 generating 1.7 petabytes of monthly traffic.

Globally, 45 percent of total mobile data traffic was offloaded onto the fixed network through Wi-Fi or femtocell in 2013. In 2013, 1.2 exabytes of mobile data traffic were offloaded onto the fixed network each month. Without offload, mobile data traffic would have grown 98 percent rather than 81 percent in 2013.

Per-user iOS mobile devices (smartphones and tablets) data usage marginally surpassed that of Android mobile devices data usage. By the end of 2013, average iOS consumption exceeded average Android consumption in North America and Western Europe.

In 2013, 18 percent of mobile devices were potentially IPv6-capable. This estimate is based on network connection speed and OS capability.

In 2013, the number of mobile-connected tablets increased 2.2-fold to 92 million, and each tablet generated 2.6 times more traffic than the average smartphone. In 2013, mobile data traffic per tablet was 1,374 MB per month, compared to 529 MB per month per smartphone.

There were 149 million laptops on the mobile network in 2013, and each laptop generated 4.6 times more traffic than the average smartphone. Mobile data traffic per laptop was 2.45 GB per month in 2013, up 17 percent from 2.1 GB per month in 2012.

Average nonsmartphone usage increased 39 percent to 10.8 MB per month in 2013, compared to 7.8 MB per month in 2012. Basic handsets still make up the vast majority of handsets on the network (73 percent).

The Mobile Network Through 2018

Mobile data traffic will reach the following milestones within the next five years.

• Monthly global mobile data traffic will surpass 15 exabytes by 2018.

• The number of mobile-connected devices will exceed the world’s population by 2014.

• The average mobile connection speed will surpass 2 Mbps by 2016.

• Due to increased usage on smartphones, smartphones will reach 66 percent of mobile data traffic by 2018.

• Monthly mobile tablet traffic will surpass 2.5 exabyte per month by 2018.

• Tablets will exceed 15 percent of global mobile data traffic by 2016.

• 4G traffic will be more than half of the total mobile traffic by 2018.

• There will be more traffic offloaded from cellular networks (on to Wi-Fi) than remain on cellular networks by 2018.

Global mobile data traffic will increase nearly 11-fold between 2013 and 2018. Mobile data traffic will grow at a compound annual growth rate (CAGR) of 61 percent from 2013 to 2018, reaching 15.9 exabytes per month by 2018.

By the end of 2014, the number of mobile-connected devices will exceed the number of people on earth, and by 2018 there will be nearly 1.4 mobile devices per capita. There will be over 10 billion mobile-connected devices by 2018, including machine-to-machine (M2M) modules-exceeding the world’s population at that time (7.6 billion).

Mobile network connection speeds will increase two-fold by 2018. The average mobile network connection speed (1,387 Kbps in 2013) will exceed 2.5 megabits per second (Mbps) by 2018.

By 2018, 4G will be 15 percent of connections, but 51 percent of total traffic. By 2018, a 4G connection will generate 6 times more traffic on average than a non-4G connection.

By 2018, over half of all devices connected to the mobile network will be “smart” devices. Globally, 54 percent of mobile devices will be smart devices by 2018, up from 21 percent in 2013. The vast majority of mobile data traffic (96 percent) will originate from these smart devices by 2018, up from 88 percent in 2013.

By 2018, 48 percent of all global mobile devices could potentially be capable of connecting to an IPv6 mobile network. Over 4.9 billion devices will be IPv6-capable by 2018.

Over two-thirds of the world’s mobile data traffic will be video by 2018. Mobile video will increase 14-fold between 2013 and 2018, accounting for 69 percent of total mobile data traffic by the end of the forecast period.

By 2018, mobile-connected tablets will generate nearly double the traffic generated by the entire global mobile network in 2013. The amount of mobile data traffic generated by tablets by 2018 (2.9 exabytes per month) will be 1.9 times higher than the total amount of global mobile data traffic in 2013 (1.5 exabytes per month).

The average smartphone will generate 2.7 GB of traffic per month by 2018, a 5-fold increase over the 2013 average of 529 MB per month. By 2018, aggregate smartphone traffic will be 11 times greater than it is today, with a CAGR of 63 percent.

By 2018, more than half of all traffic from mobile-connected devices (almost 17 exabytes) will be offloaded to the fixed network by means of Wi-Fi devices and femtocells each month. Without Wi-Fi and femtocell offload, total mobile data traffic would grow at a CAGR of 65 percent between 2013 and 2018 (12-fold growth), instead of the projected CAGR of 61 percent (11-fold growth).

The Middle East and Africa will have the strongest mobile data traffic growth of any region at 70 percent CAGR. This region will be followed by Central & Eastern Europe at 68 percent and Asia Pacific at 67 percent.

Appendix A summarizes the details and methodology of the VNI forecast.

2013 Year in Review

Global mobile data traffic grew 81 percent in 2013, a rebound over the 2012 slowdown in mobile traffic. Growth rates varied widely by region. All of the emerging market regions experienced a doubling of mobile data traffic in 2013. (Middle East and Africa grew 107 percent, Latin America grew 105 percent, and Central and Eastern Europe grew 99 percent.) Mobile data traffic grew 86 percent in Asia Pacific, 77 percent in North America, and 57 percent in Western Europe.

Table 1. Examples of Mobile Data Traffic Growth in 2013

Region Mobile Traffic Growth Examples
Korea As reported by Korean regulator KCC, mobile data traffic on 2G, 3G, and 4G networks increased approximately 70% between 3Q 2012 and 3Q 2013.
China Mobile data traffic of China’s top 3 mobile operators grew 90% in 2012 and 72% from mid-2012 to mid-2013.
Japan Mobile data traffic grew 92% in 2012 and 66% from 3Q 2012 to 3Q 2013, according to Japan’s Ministry of Internal Affairs and Communications.
India Bharti Airtel reported mobile data traffic growth of 112% between 3Q 2012 and 3Q 2013.

Reliance Communications reported mobile data traffic growth of 116% between 3Q 2012 and 3Q 2013.

Australia As reported by Australian regulator ACMA, mobile data traffic grew 47% from mid-2012 to mid-2013.
Italy As reported by Italian regulator AGCOM, mobile traffic in Italy in 3Q13 was up 34% year-over-year.
France As reported by French regulator ARCEP, mobile traffic in France was up 60% from 2Q 2013 to 2Q 2012.
Germany As reported by German regulator BNA, mobile traffic in Germany grew 40% in 2012.
Sweden As reported by Swedish regulator PTS, mobile traffic in Sweden grew 69 percent from mid-2012 to mid-2013.
Russia Vimpelcom reported mobile data traffic growth of 106% from 3Q 2012 to 3Q 2013.
Other Vodafone’s year-over-year global mobile traffic growth was 60% from 1Q FY12 to 1Q FY13.

Vodafone’s European traffic grew 35% during fiscal year 2012-2013, up from 18% the previous fiscal year.


Global Mobile Data Traffic, 2013 to 2018

Overall mobile data traffic is expected to grow to 15.9 exabytes per month by 2018, nearly an 11-fold increase over 2013. Mobile data traffic will grow at a CAGR of 61 percent from 2013 to 2018 (Figure 1).

Figure 1. Cisco Forecasts 15.9 Exabytes per Month of Mobile Data Traffic by 2018

The Asia Pacific and North America regions will account for almost two-thirds of global mobile traffic by 2018, as shown in Figure 2. Middle East and Africa will experience the highest CAGR of 70 percent, increasing 14-fold over the forecast period. Central and Eastern Europe will have the second highest CAGR of 68 percent, increasing 13-fold over the forecast period. The emerging market regions of Asia Pacific and Latin America will have CAGRs of 67 percent and 66 percent respectively.

Figure 2. Global Mobile Data Traffic Forecast by Region

Top Global Mobile Networking Trends

The sections that follow identify nine major trends contributing to the growth of mobile data traffic.

1. Transitioning to Smarter Mobile Devices

2. Measuring Internet of Everything Adoption-Emerging Wearable Devices

3. Analyzing Mobile Applications-Video Dominance

4. Profiling Bandwidth Consumption by Device

5. Assessing Mobile Traffic/Offload by Access Type (2G, 3G, and 4G)

6. Comparing Mobile Network Speeds

7. Reviewing Tiered Pricing-Managing Top Mobile Users

8. Adopting IPv6-Beyond an Emerging Protocol

9. Defining Mobile “Prime Time”-Peak vs. Average Usage

Trend 1: Transitioning to Smarter Mobile Devices

The increasing number of wireless devices that are accessing mobile networks worldwide is one of the primary contributors to global mobile traffic growth. Each year several new devices in different form factors and increased capabilities and intelligence are being introduced in the market. Over half a billion (526 million) mobile devices and connections were added in 2013. Global mobile devices and connections grew, in 2013, to 7 billion, up from 6.5 billion in 2012. Globally, mobile devices and connections will grow to 10.2 billion by 2018 at a CAGR of 8 percent (Figure 3). By 2018, there will be 8.2 billion handheld or personal mobile-ready devices and 2 billion machine-to-machine connections (e.g., GPS systems in cars, asset tracking systems in shipping and manufacturing sectors, or medical applications making patient records and health status more readily available, et al). Regionally, North America and Western Europe are going to have the fastest growth in mobile devices and connections with 12 percent and 10 percent CAGR from 2013 to 2018 respectively.

Figure 3. Global Mobile Devices and Connections Growth

We see a rapid decline in the share of nonsmartphones from more than 66 percent in 2013 (4.7 billion) to less than 34 percent by 2018 (3.5 billion). The most noticeable growth is going to occur in tablets, followed by machine-to-machine connections (M2M), both growing nearly six-fold over the forecast period. Tablets are going to grow at 41 percent CAGR from 2013 to 2018, and the M2M category is going to grow at 43 percent CAGR during the same period.

While there is an overall growth in the number of mobile devices and connections, there is also a visible shift in the device mix. Throughout the forecast period, we see that the device mix is getting smarter with an increasing number of devices with higher computing resources, network connection capabilities that create a growing demand for more capable and intelligent networks. We define smart devices and connections as those having advanced computing and multimedia capabilities with a minimum of 3G connectivity. As mentioned previously, 526 million mobile devices and connections were added in 2013, and smartphones accounted for 77 percent of that growth at 406 million net adds. The share of smart devices and connections as a percentage of the total will increase from 21 percent in 2013 to more than half, at 54 percent, by 2018, growing 3.8 fold during the forecast period (Figure 4).

Figure 4. Global Growth of Smart Mobile Devices and Connections

Although it is a global phenomenon, some regions are ahead in this device mix conversion. North America will have over 90 percent of its installed base converted to smart devices and connections, followed by Western Europe with 83 percent smart devices and connections (Table 2).

Table 2. Regional Share of Smart Devices and Connections (Percent of the Regional Total)

Region 2013 2018
North America 65% 93%
Western Europe 45% 83%
Central and Eastern Europe 15% 61%
Latin America 14% 55%
Asia Pacific 17% 47%
Middle East and Africa 10% 36%


Source: Cisco VNI Mobile, 2014

Figure 5 shows the impact of mobile smart devices and connections growth on global traffic. Globally, smart traffic is going to grow from 88 percent of the total global mobile traffic to 96 percent by 2018. This is significantly higher than the ratio of smart devices and connections (54% by 2018), because on average a smart device generates much higher traffic than a nonsmart device.

Figure 5. Effect of Smart Mobile Devices and Connections Growth on Traffic

Mobile devices and connections are not only getting smarter in their computing capabilities but are also evolving from lower-generation network connectivity (2G) to higher-generation network connectivity (3G, 3.5G, and 4G or LTE). When device capabilities are combined with faster, higher bandwidth and more intelligent networks, it leads to wide adoption of advanced multimedia applications that contribute to increased mobile and Wi-Fi traffic.

The explosion of mobile applications and phenomenal adoption of mobile connectivity by end users, on the one hand, and the need for optimized bandwidth management and network monetization, on the other hand, is fueling the growth of global 4G deployments and adoption. Service providers around the world are busy rolling out 4G networks to help them meet the growing end-user demand for more bandwidth, higher security, and faster connectivity on the move (Appendix B).

Globally, the relative share of 3G and 3.5G-capable devices and connections will surpass 2G-capable devices and connections by 2016 (48 percent and 44 percent relative share). By 2018, 15 percent of all global devices and connections will be 4G capable (Figure 6). The global mobile 4G connections will grow from 203 million in 2013 to 1.5 billion by 2018 at a CAGR of 50 percent.

Figure 6. Global Mobile Devices and Connections by 2G, 3G, and 4G

In addition to transition from 2G to 3G, 4G deployment is also a global phenomenon. In fact, by 2018, North America will have the majority (51 percent) of its mobile devices and connections with 4G capability, surpassing 3G-capable devices and connections. Western Europe (24 percent) will have the second highest ratio of 4G connections by 2018 (Appendix B). Among countries, Japan will have over 56 percent of the country’s total connections on 4G by 2018, with Korea having 54 percent of all its connections on 4G by 2018. The United States is going to lead the world in terms of its share of the total global 4G connections with 23 percent of global 4G connections.

The growth in 4G, with its higher bandwidth, lower latency, and increased security, will help regions bridge the gap between their mobile and fixed network performance. This will lead to even higher adoption of mobile technologies by end users, making access to any content on any device from anywhere more of a reality.

Trend 2: Measuring Internet of Everything Adoption-Emerging Wearable Devices

The phenomenal growth in smarter end-user devices and M2M connections is a clear indicator of the growth of the Internet of everything (IoE), which is bringing together people, processes, data, and things to make networked connections more relevant and valuable. In this section, we will focus on the continued growth of M2M connections and the emerging trend of wearable devices. Both M2M and wearable devices are making computing and connectivity very pervasive in our day-to-day lives.

M2M connections-such as home and office security and automation, smart metering and utilities, maintenance, building automation, automotive, healthcare and consumer electronics, and more-are being used across a broad spectrum of industries, as well as in the consumer segment. As real-time information monitoring helps companies deploy new video-based security systems, while also helping hospitals and healthcare professionals remotely monitor the progress of their patients, bandwidth-intensive M2M connections are becoming more prevalent. Globally, M2M connections will grow from 341 million in 2013 to over 2 billion by 2018, a 43 percent CAGR. M2M capabilities similar to end-user mobile devices are migrating from 2G to 3G and 4G technologies. In 2013, 71 percent of global mobile M2M connections were connected using 2G connectivity, while 28 percent used 3G, and less than 0.5 percent used 4G. By 2018, only 35 percent of M2M modules will have 2G connectivity; 51 percent will have 3G connectivity; and 14 percent will have 4G connectivity (Figure 7).

Figure 7. Global Machine-to-Machine Growth and Migration from 2G to 3G and 4G

An important factor contributing to the growing adoption of IoE is the emergence of wearable devices, a category with high growth potential. Wearable devices, as the name suggests, are devices that can be worn on a person, which have the capability to connect and communicate to the network either directly through embedded cellular connectivity or through another device (primarily a smartphone) using Wi-Fi, Bluetooth or another technology. These devices come in various shapes and forms, ranging from smart watches, smart glasses, heads-up displays (HUD), health and fitness trackers, health monitors, wearable scanners and navigation devices, smart clothing, and so forth. The growth in these devices has been fuelled by enhancements in technology that have supported compression of computing and other electronics (making the devices light enough to be worn). These advances are being combined with fashion to match personal styles, especially in the consumer electronics segment, along with network improvements and the growth of applications, such as location-based services and augmented reality. While there have been vast technological improvements to make wearables possible as a significant device category, the embedded cellular connectivity still has some barriers, such as technology, regulatory, and health concerns, to overcome before it becomes widely available and adopted.

By 2018, we estimate that, there will be 177 million wearable devices globally, growing eight-fold from 22 million in 2013 at a CAGR of 52 percent (Figure 8). As mentioned earlier, there will be limited embedded cellular connectivity in wearables through the forecast period. Only 13 percent will have embedded cellular connectivity by 2018, up from 1 percent in 2013. Currently, we do not include wearables as a separate device and connections category because it is at a nascent stage, so there is a noted overlap with the M2M category. We will continue to monitor this segment, and as the category grows and becomes more significant, we may break it out in future forecast iterations.

Figure 8. Global Connected Wearable Devices

Regionally, North America will lead through the forecast period in its relative share of wearables, with a 42 percent share in 2013 going to 34 percent by 2018 (Appendix B). Other regions with significant share include Western Europe with 25 percent share in 2013, growing to 26 percent by 2018, and Asia Pacific with 21 percent share, growing to 25 percent by 2018.

The wearables category will have a tangible impact on mobile traffic, because even without embedded cellular connectivity, they can connect to mobile networks through smartphones. Globally, traffic from wearables will account for 0.5 percent of smartphone traffic by 2018 (Figure 9).Globally, traffic from wearable devices will grow 36-fold from 2013 to 61 petabytes per month by 2018 (CAGR 105 percent). Globally, traffic from wearable devices will account for 0.4 percent of total mobile data traffic by 2018, compared to 0.1 percent at the end of 2013.

Figure 9. Global Wearable Devices Traffic Impact

Trend 3: Analyzing Mobile Applications-Video Dominance

Because mobile video content has much higher bit rates than other mobile content types, mobile video will generate much of the mobile traffic growth through 2018. Mobile video will grow at a CAGR of 69 percent between 2013 and 2018, the highest growth rate of any mobile application category that we forecast, other than machine-to-machine traffic. Of the 15.9 exabytes per month crossing the mobile network by 2018, 11 exabytes will be due to video (Figure 10). Mobile video represented more than half of global mobile data traffic beginning in 2012, indicating that it is having an immediate impact on traffic today, not just in the future.

Figure 10. Mobile Video Will Generate Over 69 Percent of Mobile Data Traffic by 2018

Because many Internet video applications can be categorized as cloud applications, mobile cloud traffic follows a curve similar to video. Mobile devices have memory and speed limitations that might prevent them from acting as media consumption devices, were it not for cloud applications and services. Cloud applications and services such as Netflix, YouTube, Pandora, and Spotify allow mobile users to overcome the memory capacity and processing power limitations of mobile devices. Globally, cloud applications will account for 90 percent of total mobile data traffic by 2018, compared to 82 percent at the end of 2013 (Figure 11). Mobile cloud traffic will grow 12-fold from 2013 to 2018, a compound annual growth rate of 64 percent.

Figure 11. Cloud Applications Will Account for 90 Percent of Mobile Data Traffic by 2018

Trend 4: Profiling Bandwidth Consumption by Device

The proliferation of high-end handsets, tablets, and laptops on mobile networks is a major traffic generator, because these devices offer the consumer content and applications not supported by previous generations of mobile devices. As shown in Figure 12, a single smartphone can generate as much traffic as 49 basic-feature phones; a tablet as much traffic as 127 basic-feature phones; and a single laptop can generate as much traffic as 227 basic-feature phones.

Figure 12. High-End Devices Significantly Multiply Traffic

Average traffic per device is expected to increase rapidly during the forecast period, as shown in Table 3.

Table 3. Summary of Per-Device Usage Growth, MB per Month

Device Type 2013 2018
Nonsmartphone 10.8 45
M2M Module 61 451
Wearable Device 78 345
Smartphone 529 2,672
4G Smartphone 1,984 5,371
Tablet 1,374 5,609
4G Tablet 2,410 9,183
Laptop 2,455 5,095


Source: Cisco VNI Mobile, 2014

The growth in usage per device outpaces the growth in the number of devices. As shown in Table 4, the growth rate of mobile data traffic from new devices is two to five times greater than the growth rate of users.

Table 4. Comparison of Global Device Unit Growth and Global Mobile Data Traffic Growth

Device Type Growth in Devices,
2013-2018 CAGR
Growth in Mobile Data Traffic,
2013-2018 CAGR
Smartphone 18% 63%
Tablet 41% 87%
Laptop 13% 30%
M2M Module 43% 113%


Source: Cisco VNI Mobile, 2014

A few of the main promoters of growth in average usage are described in the following list:

• As mobile network connection speeds increase, the average bit rate of content accessed through the mobile network will increase. High-definition video will be more prevalent, and the proportion of streamed content, as compared to side-loaded content, is also expected to increase with average mobile network connection speed.

• The shift toward on-demand video will affect mobile networks as much as it will affect fixed networks. Traffic can increase dramatically, even while the total amount of time spent watching video remains relatively constant.

• As mobile network capacity improves, and the number of multiple-device users grows, operators are more likely to offer mobile broadband packages comparable in price and speed to those of fixed broadband. This is encouraging mobile broadband substitution for fixed broadband, where the usage profile is substantially higher than average.

• Mobile devices increase an individual’s contact time with the network, and it is likely that this increased contact time will lead to an increase in overall minutes of use per user. However, not all of the increase in mobile data traffic can be attributed to traffic migration to the mobile network from the fixed network. Many uniquely mobile applications continue to emerge, such as location-based services, mobile-only games, and mobile commerce applications.

Trend 5: Assessing Mobile Traffic/Offload by Access Type (2G, 3G, and 4G)

Impact of 4G

While 3G and 3.5G account for the majority (60 percent) of mobile data traffic today, 4G will grow to represent over half of all mobile data traffic by 2018, despite a connection share of only 15 percent (Figure 13).

Currently, a 4G connection generates nearly 15 times more traffic than a non-4G connection. There are two reasons for this. The first is that many 4G connections today are for high-end devices, which have a higher average usage. The second is that higher speeds encourage the adoption and usage of high-bandwidth applications, such that a smartphone on a 4G network is likely to generate 50 percent more traffic than the same model smartphone on a 3G or 3.5G network. As smartphones come to represent a larger share of 4G connections, the gap between the average traffic of 4G devices and non-4G devices will narrow, but by 2018 a 4G connection will still generate 6 times more traffic than a non-4G connection.

Figure 13. 51 Percent of Total Mobile Data Traffic Will Be 4G by 2018


Much mobile data activity takes place within users’ homes. For users with fixed broadband and Wi-Fi access points at home, or for users served by operator-owned femtocells and picocells, a sizable proportion of traffic generated by mobile and portable devices is offloaded from the mobile network onto the fixed network. For the purposes of this study, offload pertains to traffic from dual mode devices (i.e., supports cellular and Wi-Fi connectivity; excluding laptops) over Wi-Fi and small cell networks. Offloading occurs at the user/device level when one switches from a cellular connection to Wi-Fi/small cell access. Our mobile offload projections include traffic from both public hotspots as well as residential Wi-Fi networks.

As a percentage of total mobile data traffic from all mobile-connected devices, mobile offload increases from 45 percent (1.2 exabytes/month) in 2013 to 52 percent (17.3 exabytes/month) by 2018 (Figure 14). Without offload, Global mobile data traffic would grow at a CAGR of 65 percent instead of 61 percent. Offload volume is determined by smartphone penetration, dual-mode share of handsets, percentage of home-based mobile Internet use, and percentage of dual-mode smartphone owners with Wi-Fi fixed Internet access at home.

Figure 14. 52 Percent of Total Mobile Data Traffic Will Be Offloaded by 2018

The amount of traffic offloaded from smartphones will be 51 percent by 2018, and the amount of traffic offloaded from tablets will be 69 percent by 2018.

A supporting trend is the growth of cellular connectivity for devices such as tablets which in their earlier generation were limited to Wi-Fi connectivity only. With increased desire for mobility and mobile carriers offer of data plans catering to multi-device owners, we find that the cellular connectivity is on a rise albeit cautiously as the end users are testing the waters. As a point in case, we estimate that by 2018, 42 percent of all tablets will have a cellular connection up from 34 percent in 2013 (Figure 15).

Figure 15. 42 Percent of Global Tablets Will Be Cellular Connected by 2018

Some have speculated that Wi-Fi offload will be less relevant once 4G networks are in place because of the faster speeds and more abundant bandwidth. However, 4G networks will attract high-usage devices such as advanced smartphones and tablets, and it appears that 4G plans will be subject to data caps similar to 3G plans. For these reasons, Wi-Fi offload is higher on 4G networks than on lower speed networks, now and in the future according to our projections. The amount of traffic offloaded from 4G was 54 percent at the end of 2013 and will be 56 percent by 2018 (Figure 16). The amount of traffic offloaded from 3G will be 49 percent by 2018, and the amount of traffic offloaded from 2G will be 40 percent by 2018.

Figure 16. Mobile Data Traffic and Offload Traffic, 2018

Trend 6: Comparing Mobile Network Speeds

Globally, the average mobile network connection speed in 2013 was 1,387 Kbps. The average speed will grow at a compound annual growth rate of 13 percent, and will exceed 2.5 Mbps by 2018. Smartphone speeds, generally third-generation (3G) and higher, are currently almost three times higher than the overall average. Smartphone speeds will nearly double by 2018, reaching 7 Mbps.

There is anecdotal evidence to support the idea that usage increases when speed increases, although there is often a delay between the increase in speed and the increased usage, which can range from a few months to several years. The Cisco VNI Forecast relates application bit rates to the average speeds in each country. Many of the trends in the resulting traffic forecast can be seen in the speed forecast, such as the high growth rates for developing countries and regions relative to more developed areas (Table 5).

Table 5. Projected Average Mobile Network Connection Speeds (in Kbps) by Region and Country

2013 2013 2014 2015 2016 2018 CAGR
Global speed: All handsets 1,387 1,676 1,908 2,147 2,396 2,509 13%
Global speed: Smartphones 3,983 4,864 5,504 6,132 6,756 7,044 12%
Global speed: Tablets 4,591 5,584 6,298 6,483 8,018 8,998 14%
By Region
Middle East & Africa 529 605 675 753 832 900 11%
Central & Eastern Europe 1,351 1,446 1,711 1,945 2,128 2,269 11%
Latin America 684 734 793 856 924 999 8%
Western Europe 1,585 1,735 1,946 2,183 2,452 3,003 14%
Asia-Pacific 1,327 1,492 1,617 1,728 1,863 1,992 8%
North America 1,728 2,010 2,304 2,620 2,988 4,549 21%


Source: Cisco VNI Mobile, 2014

Current and historical speeds are based on data from Cisco’s GIST (Global Internet Speed Test) application and Ookla’s Speedtest. Forward projections for mobile data speeds are based on third-party forecasts for the relative proportions of 2G, 3G, 3.5G, and 4G among mobile connections through 2018. For more information about Cisco GIST, please visit

The speed at which data can travel to and from a mobile device happen in two places: the infrastructure speed capability outside the device, and the connectivity speed from the network capability inside the device. These speeds are actual and modeled end user speeds and not theoretical speeds that the devices, connection or technology is capable of providing. There are several variables that affect the performance of a mobile connection. Roll out of 2G/3G/4G in various countries and regions, technology used by the cell towers, spectrum availability, terrain, signal strength, and number of devices sharing a cell tower. The type of application being used by the end user is also an important factor. Download speed, upload speed and latency characteristics vary widely depending on the type of application, be it video, radio or instant messaging.

Figure 17. Mobile Speeds by Technology 2G vs. 3G vs. 4G

4G Speeds will be 6 times higher than that of an average mobile connection by 2018. In comparison, 3G speeds will be twice as fast as the average mobile connection by 2018.

Figure 18. Mobile Speeds by Device

Trend 7: Reviewing Tiered Pricing-Managing Top Mobile Users

An increasing number of service providers worldwide are moving from unlimited data plans to tiered mobile data packages. To make an initial estimate of the impact of tiered pricing on traffic growth, we repeated a case study based on the data of two Tier 1 global service providers from mature mobile markets. The study tracks data usage from the timeframe of the introduction of tiered pricing three years ago. The findings in this study are based on Cisco’s analysis of data provided by a third-party data analysis firm. This firm maintains a panel of volunteer participants who have given the company access to their mobile service bills, including KB of data usage. The data in this study reflects usage associated with over 38,889 devices and spans 12 months (October 2012 through September 2013) and also refers to the study from the previous update for longer term trends. The overall study spans three years. Cisco’s analysis of the data consists of categorizing the pricing plans, operating systems, devices, and users; incorporating additional third-party information on device characteristics; and performing exploratory and statistical data analysis. While the results of the study represent actual data from Tier 1 mobile data operators, global forecasts that include emerging markets, and Tier 2 providers may lead to lower estimates.

Over the period of the nearly 3-year study, the percentage of tiered plans compared to all data plans increased from 4 percent to 55 percent, while unlimited plans dropped from 81 percent to 45 percent. This has not, however, constrained usage patterns. From 2012 to 2013, average usage per device on a tiered plan grew from 922 MB per month to 1,081 MB per month, while usage per device of unlimited plans grew at from a higher base of 1,261 MB per month to 1,890 MB per month.

However, tiered plans are effective. There is a narrowing of the bandwidth consumption gap between tiered and unlimited data plan connections, showing the general increase in consumption of mobile data traffic due to the increased consumption of services such as Pandora, YouTube, Facebook, and Netflix. Unlimited plans have promoted the adoption of mobile applications and increased web usage through mobile broadband.

Tiered pricing plans are often designed to constrain the heaviest mobile data users, especially the top 1 percent of mobile data consumers. An examination of heavy mobile data users reveals that the top 1 percent of mobile users is actually the top 3.5 percent, because the top 1 percent of users varies each month. For example, for a mobile data subscriber base of 1000 users; the top 1 percent is 10 users. However, the same set of 10 users does not appear in the top 1 percent category in each month; rather, a larger set of 35 subscribers rotates though the top 1 percent. This top 3.5 percent are the users who have the potential of being in the top 1 percent bracket in any given month and substitute for each other in subsequent months. The trend is due to the nature of consumption of mobile data applications.

The usage per month of the average top 1 percent of mobile data users has been steadily decreasing compared to overall usage. At the beginning of the 3-year study, 52 percent of the traffic was generated by the top 1 percent. At the end of the three year time frame, the top 1 percent generated 10 percent of the overall traffic per month compared to 16 percent in September 2012 (Figure 19). The top 10 percent of mobile users generate as much traffic as the remaining 90 percent of mobile data traffic (Figure 20).

Figure 19. Top 1 Percent Generates 52 Percent of Monthly Data Traffic in Jan 2010 Compared to 10 Percent in Sept 2013

Figure 20. Top 10 Percent Consumes Nearly As Much As the Remaining 90 Percent

Figure 21. Remaining 99 Percent Growing Faster Than Top 1 Percent

Additional evidence that tiered pricing plans are effectively constraining the top 1 percent of mobile users, and that the growth is being made up by those outside the top 1 percent, is that the usage of the remaining 99 percent is growing much more rapidly than the top 1 percent (Figure 21). With the introduction of new larger screen smartphones and tablets, there is continued increase in usage in terms of megabytes per month per user in all the top tiers (Figure 22).

Figure 22. All Top Tiers Increase in Absolute Usage (MB per Month) from 2011 to 2013

The proportion of mobile users generating more than 2 gigabytes per month has increased significantly over the past year, reaching 24 percent of users towards the end of 2013 (Figure 23).

Figure 23. 3 Percent of Users Consume 5 GB per Month and 24 Percent Consume over 2 GB per Month

More detail on the tiered pricing case study is available in Appendix C.

iOS Marginally Surpasses Android in Data Usage

At the beginning of the three year tiered pricing case study, Android data consumption was equal to if not higher than other smartphone platforms. However, Apple-based devices have now caught up and their data consumption is marginally higher than that of Android devices in terms of megabytes per month per connection usage (Figure 24).

Figure 24. Megabytes per Month by Operating System

Tiered plans outnumber unlimited plans; unlimited plans continue to lead in data consumption.

Figure 25. Tiered vs. Unlimited Plans

The number of shared plans is increasing; there is no clear effect on usage during the short time frame of the study.

Figure 26. Shared vs. Regular Data Plans

More detail on consumption by operating system is available in Appendix C.

Trend 8: Adopting IPv6-Beyond an Emerging Protocol

The transition to IPv6 is well underway, which helps connect and manage the proliferation of newer-generation devices that are contributing to mobile network usage and data traffic growth. Continuing the Cisco VNI focus on IPv6, the Cisco VNI 2013-2018 Mobile Data Traffic Forecast provides an update on IPv6-capable mobile devices and connections and the potential for IPv6 mobile data traffic.

Focusing on the high-growth mobile-device segments of smartphones and tablets, the forecast projects that globally 79 percent of smartphones and tablets (3.5 billion) will be IPv6 capable by 2018 (up from 46 percent or 837 million smartphones and tablets in 2013). This estimation is based on OS support of IPv6 (primarily Android and iOS) and the accelerated move to higher-speed mobile networks (3.5G or higher) capable of enabling IPv6. (This forecast is intended as a projection of the number of IPv6-capable mobile devices, not mobile devices with an IPv6 connection actively configured by the ISP.)

Figure 27. Global IPv6-Capable Smartphones and Tablets Reach 3.5 Billion by 2018

For all mobile devices and connections, the forecasts project that, globally, nearly half (48 percent) will be IPv6-capable by 2018, up from 18 percent (1.3 billion) in 2013. M2M emerges as a key segment of growth for IPv6-capable devices, reaching nearly 600 million by 2018, a 46-fold increase during the forecast period. With its capability to vastly scale IP addresses and manage complex networks, IPv6 is critical in supporting the IoE of today and in the future. (Refer to Table 15 in Appendix D for more device detail.)

Regionally, Asia Pacific will lead throughout the forecast period with the highest number of IPv6-capable devices and connections, reaching 2.2 billion by 2018. Middle East and Africa and Asia-Pacific will have the highest growth rates during the forecast period, at 35 percent CAGR and 34 percent CAGR respectively. (Refer to Table 16 in Appendix D for more regional detail.)

Figure 28. Global IPv6-Capable Mobile Devices Reach 4.9 Billion by 2018

Considering the significant potential for mobile device IPv6 connectivity, the Cisco VNI Mobile Forecast provides an estimation for IPv6 network traffic based on a graduated percentage of IPv6-capable devices becoming actively connected to an IPv6 network. Looking to 2018, if 50 percent of IPv6-capable devices are connected to an IPv6 network, the forecast estimates that, globally, IPv6 traffic will amount to 6.6 exabytes per month or 40 percent of total mobile data traffic, a 73-fold growth from 2013 to 2018.

Figure 29. Projected IPv6 Mobile Data Traffic Forecast 2013-2018

For additional views on the latest IPv6 deployment trends, visit the Cisco 6Lab site. The Cisco 6Lab analysis includes current statistics by country on IPv6 prefix deployment, IPv6 web content availability, and estimations of IPv6 users. With the convergence of IPv6 device capability, content availability, and network deployment, the discussion of IPv6 moves from “what if” to “how soon will” service providers and end users realize the potential IPv6 has to offer.

Trend 9: Defining Mobile “Prime Time”-Peak vs. Average Usage

Mobile video applications have a “prime time” in that they are predominantly used during certain times of day. Web and general data usage tends to occur throughout the day, but video consumption is highest in the evening. Video therefore has a higher peak-to-average ratio than web and data. Live video and video communications have higher peak-to-average ratios than video-on-demand. As the mobile network application mix shifts towards video, and as the video mix increasingly includes live video and video communication, the overall mobile data peak-to-average ratio increases. Busy hour mobile traffic is growing at a slightly higher pace than average hour traffic, and by 2018 mobile busy hour traffic will be 83 percent higher than average hour traffic by 2018, compared to 66 percent in 2013 (Figure 30).

Figure 30. Mobile Busy Hour Is 66% Higher Than Average Hour in 2013, 83% by 2018

The faster growth of busy hour traffic is not as pronounced on mobile networks as on fixed networks because mobile networks never had a large amount of peer-to-peer file sharing traffic, which brought down the peak-to-average ratio on fixed networks until video overtook peer-to-peer as the dominant application. Even though the trend is less pronounced, mobile operators will need to plan for a mobile busy hour compound annual growth rate of 64 percent between 2013 and 2018.


Mobile data services are well on their way to becoming necessities for many network users. Mobile voice service is already considered a necessity by most, and mobile data, video, and TV services are fast becoming an essential part of consumers’ lives. Used extensively by consumer as well as enterprise segments, with impressive uptakes in both developed and emerging markets, mobility has proven to be transformational. Mobile subscribers are growing rapidly and bandwidth demand due to data and video is increasing. Mobile M2M connections continue to increase. The next 5 years are projected to provide unabated mobile video adoption despite uncertain macroeconomic conditions in many parts of the world. Backhaul capacity must increase so mobile broadband, data access, and video services can effectively support consumer usage trends and keep mobile infrastructure costs in check.

Deploying next-generation mobile networks requires greater service portability and interoperability. With the proliferation of mobile and portable devices, there is an imminent need for networks to allow all these devices to be connected transparently, with the network providing high-performance computing and delivering enhanced real-time video and multimedia. This openness will broaden the range of applications and services that can be shared, creating a highly enhanced mobile broadband experience. The expansion of wireless presence will increase the number of consumers who access and rely on mobile networks, creating a need for greater economies of scale and lower cost per bit.

As many business models emerge with new forms of advertising, media and content partnerships, mobile services including M2M, live gaming, and (in the future) augmented reality, a mutually beneficial situation needs to be developed for service providers and over-the-top providers. New partnerships, ecosystems, and strategic consolidations are expected as mobile operators, content providers, application developers, and others seek to monetize the video traffic that traverses mobile networks. Operators must solve the challenge of effectively monetizing video traffic while increasing infrastructure capital expenditures. They must become more agile and able to quickly change course and provide innovative services to engage the Web 3.0 consumer. While the net neutrality regulatory process and business models of operators evolve, there is an unmet demand from consumers for the highest quality and speeds. As wireless technologies aim to provide experiences formerly only available through wired networks, the next few years will be critical for operators and service providers to plan future network deployments that will create an adaptable environment in which the multitude of mobile-enabled devices and applications of the future can be deployed.

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Appendix A: The Cisco VNI Global Mobile Data Traffic Forecast

Table 6 shows detailed data from the Cisco VNI Global Mobile Data Traffic Forecast. The portable device category includes laptops with mobile data cards, USB modems, and other portable devices with embedded cellular connectivity.

Table 6. Global Mobile Data Traffic, 2013-2018

2013 2014 2015 2016 2017 2018 CAGR
By Application Category (TB per Month)
Data 606,405 957,382 1,437,249 2,073,797 2,832,137 3,531,107 42%
File Sharing 66,671 127,235 221,808 308,643 391,641 466,347 48%
Video 793,944 1,458,730 2,579,242 4,370,458 7,094,943 10,956,123 69%
M2M 20,736 49,286 113,415 246,198 490,226 907,472 113%
By Device Type (TB per Month)
Nonsmartphones 50,425 68,087 91,030 118,901 143,427 154,258 25%
Smartphones 923,361 1,684,096 2,883,253 4,679,786 7,217,671 10,534,617 63%
Laptops 365,011 500,827 678,627 882,051 1,117,171 1,365,892 30%
Tablets 127,027 287,996 581,401 1,065,826 1,829,859 2,881,415 87%
M2M 20,736 49,286 113,415 246,198 490,226 907,472 113%
Other Portable Devices 1,196 2,341 3,987 6,333 10,593 17,394 71%
By Region (TB per Month)
North America 388,583 624,586 969,032 1,453,312 2,100,830 2,953,875 50%
Western Europe 253,679 389,397 592,818 888,378 1,310,517 1,900,486 50%
Asia Pacific 523,918 953,085 1,670,216 2,777,483 4,441,514 6,717,828 67%
Latin America 91,863 177,273 307,822 505,265 789,313 1,158,090 66%
Central and Eastern Europe 124,059 241,016 434,096 723,186 1,135,470 1,641,205 68%
Middle East and Africa 105,655 207,277 377,731 651,472 1,031,304 1,489,565 70%
Total (TB per Month)
Total Mobile Data Traffic 1,487,756 2,592,634 4,351,714 6,999,096 10,808,947 15,861,049 61%


Source: Cisco, 2014

The Cisco VNI Global Mobile Data Traffic Forecast relies in part upon data published by Informa Telecoms and Media, Strategy Analytics, Infonetics, Ovum, Gartner, IDC, Dell’Oro, Synergy, ACG Research, Nielsen, comScore, Arbitron Mobile, Maravedis and the International Telecommunications Union (ITU).

The Cisco VNI methodology begins with the number and growth of connections and devices, applies adoption rates for applications, and then multiplies the application’s user base by Cisco’s estimated minutes of use and KB per minute for that application. The methodology has evolved to link assumptions more closely with fundamental factors, to use data sources unique to Cisco, and to provide a high degree of application, segment, geographic, and device specificity.

• Inclusion of fundamental factors. As with the fixed IP traffic forecast, each Cisco VNI Global Mobile Data Traffic Forecast update increases the linkages between the main assumptions and fundamental factors such as available connection speed, pricing of connections and devices, computational processing power, screen size and resolution, and even device battery life. This update focuses on the relationship of mobile connection speeds and the KB-per-minute assumptions in the forecast model. Proprietary data from the Cisco Global Internet Speed Test (GIST) application was used as a baseline for current-year smartphone connection speeds for each country.

• Device-centric approach. As the number and variety of devices on the mobile network continue to increase, it becomes essential to model traffic at the device level rather than the connection level. This Cisco VNI Global Mobile Data Traffic Forecast update details traffic to smartphones; nonsmartphones; laptops, tablets, and netbooks; e-readers; digital still cameras; digital video cameras; digital photo frames; in-car entertainment systems; and handheld gaming consoles.

• Estimation of the impact of traffic offload. The Cisco VNI Global Mobile Data Traffic Forecast model now quantifies the effect of dual-mode devices and femtocells on handset traffic. Proprietary data from Cisco’s IBSG Connected Life Market Watch was used to model offload effects.

• Increased application-level specificity. The forecast now offers a deeper and wider range of application specificity.

Appendix B: Global 4G Networks and Connections

Table 7. Regional 4G Connections Growth

  2013 2018
  Number of 4G Connections % of Total
Number of 4G Connections % of Total
Asia Pacific 80,920,533 2.3% 667,956,749 13.1%
Central and Eastern Europe 1,846,331 0.3% 88,665,716 10.1%
Latin America 936,408 0.1% 86,222,002 9.1%
Middle East and Africa 3,648,081 0.3% 86,576,973 5.3%
North America 104,290,345 24.5% 372,559,550 50.6%
Western Europe 11,458,739 1.9% 228,065,764 24.3%
Global 203,100,439 2.9% 1,530,046,754 15.0%


Source: Cisco, 2014

Table 8. Regional Wearable Devices Growth

  2013 2018
  Number of Wearable Devices % of Global Number of Wearable Devices % of Global
Asia Pacific 4,502,201 20.8% 43,810,250 24.8%
Central and Eastern Europe 1,078,646 5.0% 9,864,884 5.6%
Latin America 984,497 4.5% 9,709,040 5.5%
Middle East and Africa 712,403 3.3% 7,955,103 4.5%
North America 9,063,366 41.8% 59,829,286 33.8%
Western Europe 5,347,081 24.7% 45,775,527 25.9%
Global 21,688,195 100.0% 176,944,090 100.0%


Source: Cisco, 2014

Appendix C: A Case Study on the Initial Impact of Tiered Pricing on Mobile Data Usage

The Changing Role of the Top 1 Percent of Mobile Data Subscribers

Three years ago, the top 1 percent of mobile data subscribers was responsible for a disproportionate amount of mobile data traffic. However, according to the data from this study, this disproportion is becoming less pronounced with time. The amount of traffic due to the top 1 percent of subscribers declined from 52 percent in January 2010 to 10 percent in September 2013.In the recent iteration of the study from October 2012 to September 2013, the amount of traffic due to the top 1 percent of the subscribers declined from 15 percent to 10 percent (Table 9).

Table 9. Percentage of Traffic by User Tier, Months October 2012-September 2013

Data Users Oct-12 Nov-12 Dec-12 Jan-13 Feb-13 Mar-13 Apr-13 May-13 Jun-13 Jul-13 Aug-13 Sept 13
% traffic due to Top 1% 15% 14% 15% 15% 19% 17% 15% 13% 14% 13% 12% 10%
% traffic due to Top 10% 49% 48% 48% 47% 59% 59% 48% 46% 46% 42% 42% 40%


Source: Cisco VNI, 2014

Table 10. Average Traffic by User Tier in MB per Month

Average MB per Month Oct-12 Nov-12 Dec-12 Jan-13 Feb-13 Mar-13 Apr-13 May-13 Jun-13 Jul-13 Aug-13 Sep-13
Top 1% 12,445 12,635 12,278 13,230 12,180 10,699 15,697 12,738 15,807 16,281 16,424 12,785
Top 5% 5,399 5,632 5,450 5,724 5,225 4,750 6,525 5,958 6,748 7,213 7,501 6,799
Top 10% 3,827 4,008 3,857 4,059 3,687 3,438 4,626 4,341 4,824 5,179 5,392 5,048
Top 20% 2,727 2,862 2,701 2,953 2,549 2,422 3,293 3,166 3,451 3,690 3,840 3,689


Source: Cisco VNI, 2014

Tiered pricing plans have lower megabyte-per-month consumption compared to unlimited plans. However, the overall measures displayed healthy growth with few signs of growth slowing, and the move to tiered pricing does not appear to have an immediate effect on overall mobile data traffic.

The number of mobile data users generating more than 2 GB per month has doubled over the course of the study, and the percentage of users generating over 200 MB per month reached 75 percent (Table 11).

Table 11. One Percent of Mobile Data Users Consume 5 GB per Month

% Oct-12 Nov-12 Dec-12 Jan-13 Feb-13 Mar-13 Apr-13 May-13 Jun-13 Jul-13 Aug-13 Sep-13
Greater than 5 GB 1% 2% 1% 2% 2% 2% 2% 2% 2% 3% 3% 3%
Greater than 2 GB 12% 13% 11% 15% 16% 17% 16% 17% 18% 20% 23% 24%
Greater than 200 MB 54% 56% 56% 56% 57% 58% 59% 60% 62% 74% 75% 75%
Greater than 20 MB 70% 72% 72% 73% 74% 74% 74% 76% 77% 93% 93% 93%


Source: Cisco VNI, 2013

The rapid increase in data usage presents a challenge to service providers who have implemented tiers defined solely in terms of usage limits. Mobile data caps that fall too far behind usage volumes may create opportunities for competitors in the market. Therefore, many service providers are creating more nuanced tiers, shared data plans and data add-ons, such as a separate charge for tethering and hotspot functionality. Such offerings tend to require less vigilance on the part of subscribers than data caps, yet still monetize scenarios that tend to have high data usage. Shared data family plans are being introduced and their effects on overall mobile data traffic are yet to be determined.

Mobile Data Traffic Volume by Operating System

While the effect of the tiered plan is clear, the average consumption per connection continues to increase for both tiered and unlimited plans Both Android- and Apple-based devices are prominent bandwidth promoters in tiered as well as unlimited plans. Android-based devices led in average megabyte-per-month usage with unlimited plans and Apple-based iOS led in usage with tiered plans (Tables 12 and 13).

Table 12. MB per Month Usage per Mobile Operating System in Unlimited Plans

Oct-12 Nov-12 Dec-12 Jan-13 Feb-13 Mar-13 Apr-13 May-13 Jun-13 Jul-13 Aug-13 Sep-13
Android 1,497 1,585 1,601 1,733 1,938 1,857 2,288 1,964 2,435 2,447 2,583 2,226
iOS 1,131 1,246 1,191 1,211 1,311 1,214 1,380 1,449 1,559 1,635 1,759 1,767
Palm OS 819 414 497 543 876 1,288 882 1,144 1,658 228 491 886
Windows 501 630 484 1,259 2,083 2,332 1,685 1,480 1,655 1,678 1,079 804
Blackberry 168 192 167 138 152 128 243 308 302 309 437 411


Source: Cisco VNI, 2014

Table 13. MB per Month Usage per Mobile Operating System in Tiered Pricing Plans

Operating System Oct-12 Nov-12 Dec-12 Jan-13 Feb-13 Mar-13 Apr-13 May-13 Jun-13 Jul-13 Aug-13 Sep-13
iOS 748 835 782 893 929 943 958 983 1,049 1,068 1,132 1,122
Android 451 468 462 515 565 582 585 583 632 944 1,000 1,051
Windows 607 531 611 632 731 829 748 760 876 926 882 976
Blackberry 229 250 203 261 292 277 263 314 345 403 445 415
Palm OS 75 130 157 199 176 213 171 224 154 261 253 244


Source: Cisco VNI, 2014

Shared data plans have been introduced in mature markets and the initial findings show lower traffic usage in shared plans; but both shared as well as regular plans continue to grow in terms of usage per month.

Table 14. Table 14: Shared vs. Regular Plans

Shared vs. Regular Plan (MB/month) Oct-12 Nov-12 Dec-12 Jan-13 Feb-13 Mar-13 Apr-13 May-13 Jun-13 Jul-13 Aug-13 Sep-13
Regular Plan 792 840 808 877 953 942 1,006 995 1,107 1,345 1,432 1,416
Shared Plan 648 733 703 725 752 742 808 798 841 908 952 946


Source: Cisco VNI, 2014

Appendix D: IPv6-Capable Devices, 2013-2018

Table 15 provides regional IPv6-capable forecast detail. Table 16 provides the segmentation of IPv6-capable devices by device type.

Table 15. IPv6-Capable Devices by Device Type, 2013-2018

Devices (K) 2013 2014 2015 2016 2017 2018 CAGR
Global 1,294,935 1,841,342 2,471,825 3,173,249 3,961,932 4,934,672 31%
Laptops 121,760 150,661 178,331 204,668 232,977 258,095 16%
M2M 12,720 37,258 86,692 176,847 329,307 586,186 115%
Nonsmartphones 309,801 435,880 544,815 577,148 548,431 546,823 12%
Other Portables 14,005 13,293 13,040 15,069 18,573 22,698 10%
Smartphones 766,567 1,089,696 1,473,281 1,944,511 2,476,418 3,049,246 32%
Tablets 70,082 114,554 175,667 255,006 356,226 471,625 46%


Source: Cisco, 2014

Table 16. IPv6-Capable Devices by Region, 2013-2018

Devices (K) 2013 2014 2015 2016 2017 2018 CAGR
Global 1,294,935 1,841,342 2,471,825 3,173,249 3,961,932 4,934,672 31%
Asia Pacific 526,332 770,169 1,063,814 1,378,683 1,759,816 2,261,164 34%
Latin America 110,158 164,800 228,623 301,506 377,236 465,365 33%
North America 185,044 237,832 291,560 357,057 424,930 503,103 22%
Western Europe 235,026 313,392 390,085 473,251 553,732 639,970 22%
Central and Eastern Europe 105,196 156,350 219,743 298,522 385,027 467,562 35%
Middle East and Africa 133,179 198,800 278,000 364,231 461,190 597,508 35%


Source: Cisco, 2014



AT&T plan to turn data caps into more cash could come to home Internet

13 Jan

Net neutrality law doesn’t explicitly bar payments to bypass data caps

AT&T announced three days ago that it would start charging content providers for the right to bypass data caps that might otherwise prevent smartphone owners from using data-hungry services like streaming video or music.The plan is opposed by those who say it violates the principles of net neutrality, that Internet service providers should treat all data equally, and that AT&T shouldn’t pick winners and losers by forcing content providers to pay for the best path to consumers.

AT&T’s plan is very likely legal, however. For one thing, the Federal Communication Commission’s Open Internet Order, which lays out the country’s net neutrality rules, places fewer restrictions on wireless Internet (your cell phone provider) than wired (your cable and/or other home Internet service).

The Open Internet Order’s laissez-faire approach to wireless made it likely that AT&T’s Sponsored Data would come to cellular networks before home broadband. However, the order may not prevent AT&T’s strategy from being used in wired Internet.

“[B]roadband providers that sought to offer pay-for-priority services would have an incentive to limit the quality of service provided to non-prioritized traffic,” the rules state. “For a number of reasons… a commercial arrangement between a broadband provider and a third party to directly or indirectly favor some traffic over other traffic in the broadband Internet access service connection to a subscriber of the broadband provider (i.e. ‘pay for priority’) would raise significant cause for concern. … [A]s a general matter, it is unlikely that pay for priority would satisfy the ‘no unreasonable discrimination’ standard.”

FCC “punted on data caps”

While the order is generally understood to prevent ISPs from demanding payments for a faster path to consumers, it doesn’t explicitly prevent them from using pay-for-play to let content providers serve up video and other data without counting against consumers’ data limits. AT&T was careful to note that “Sponsored Data will be delivered at the same speed and performance as any non-Sponsored Data content.”

“While the Open Internet Order itself talks a lot about two-sided markets, it kind of punted on data caps themselves,” Michael Weinberg, VP at consumer advocacy group Public Knowledge, told Ars.

The 2010 order “punted” in part by creating an Open Internet Advisory Committee (OIAC) to examine unresolved questions. That committee released a report in August 2013 on policy issues related to data caps and usage-based pricing.

The report “basically concluded that they and the FCC know nothing about data caps, so they were unable to make any meaningful policy recommendations,” Weinberg wrote in an e-mail. “So the real answer is that no one is really sure. One could potentially make an argument that non-exempt services were actually being blocked under a data cap (if I hit my cap and the service I want to reach hasn’t paid for an exemption, I might not be able to reach it at all), but that would really come down to specifics that may or may not exist here. If the FCC knew how data caps fit into the Open Internet Order, it wouldn’t have asked the OIAC to look at it, and if the OIAC knew, it would have given some indication of that it its report. In the absence of that, all we have is Open Internet Order language highlighting all of the problems that two-sided markets create and a pair of dice to roll. Which is not necessarily ideal.”

AT&T hasn’t revealed anything about whether it plans to bring Sponsored Data to its home Internet service. Doing so might bring a lawsuit, but the company has plenty of money for lawyers—and the lack of specifics in the Open Internet Order could make it likely that they’d win. While Weinberg noted that one could argue “that non-exempt services were actually being blocked under a data cap” when someone hits their limit, AT&T could correctly point out that consumers can simply pay for more data after reaching their limit.

TechFreedom, a libertarian think tank, agrees that the Open Internet Order is vague on the question of whether AT&T’s Sponsored Data or similar approaches would be legal in home broadband.

“It’s not entirely clear how the Open Internet Order’s non-discrimination rule would apply to Sponsored Data,” TechFreedom President Berin Szoka told Ars. “[FCC Chairman] Tom Wheeler certainly seemed, at least in his recent off-the-cuff remarks, to think that two-sided markets aren’t inconsistent with Net Neutrality.”

Wheeler’s comments haven’t settled the matter

Szoka is right about Wheeler’s comments. As we reported last month, Wheeler said he wouldn’t automatically object to companies like Netflix paying ISPs for a faster path to consumers. “I think we’re also going to see a two-sided market where Netflix might say, ‘well, I’ll pay in order to make sure that you might receive—my subscriber receives—the best possible transmission of this movie,'” Wheeler said. “I think we want to let those kinds of things evolve. We want to observe what happens from that, and we want to make decisions accordingly, but I go back to the fact that the marketplace is where these decisions ought to be made, and the functionality of a competitive marketplace dictates the degree of regulation.”

In new remarks yesterday at the Consumer Electronics Show, Wheeler left the FCC’s position on AT&T’s Sponsored Data an open question. “My attitude is, let’s take a look at what this is,” Wheeler said, according to the Washington Post. “Let’s take a look at how it operates… If it interferes with the operation of the Internet… if it develops into an anti-competitive practice… if it does have some kind of preferential treatment given somewhere, then that is cause for us to intervene.”

Post writer Brian Fung noted that “Wheeler’s comments don’t do much to clarify his cryptic positions on network neutrality.”

In another speech delivered today at the Computer History Museum in Mountain View, California, Wheeler said, “It may well be that the kind of offering AT&T has announced enables increased competition and increased efficiency—both things that benefit consumers. It is not the sort of thing that should be prohibited out of hand.”

US Rep. Anna Eshoo (D-CA) blasted AT&T’s Sponsored Data program, saying it puts AT&T “in the business of picking winners and losers on the Internet, threatening the open Internet, competition, and consumer choice. It’s exactly why net neutrality rules came to exist in the first place and why these rules should apply equally to all forms of broadband Internet service.”

Eshoo didn’t give any indication of whether she plans to file legislation to make the practice illegal. An Eshoo spokesperson did not answer our question on that topic.

Public Knowledge called on Wheeler to “open an inquiry to examine the ways that these types of schemes impact an open Internet” and said that “a pay-for-play model for companies to reach subscribers won’t help innovation, will surely stifle new entrants, and has no upside for consumers.” The group, however, noted that it previously called on the FCC to investigate the impact of data caps more than two years ago without any result.

If anything, our net neutrality rules are more likely to become less restrictive than more restrictive. Verizon filed a lawsuit claiming the entire Open Internet Order is invalid, and observers of the case believe the US Court of Appeals for the District of Columbia Circuit is likely to strike down portions of the law. A ruling could come at any time.

“[T]o the extent the DC Circuit interprets the non-discrimination rule to even potentially bar arrangements like this [AT&T Sponsored Data], the court seems highly likely to strike down either that rule or the entire order as an illegal imposition of common carriage regulation,” Szoka told Ars. “If the Court doesn’t clearly reject the FCC’s statutory authority claims, it might leave the FCC free to try again with new rules that would allow things like Sponsored Content but require that (as the FCC did with data roaming) the terms offered be reasonable and non-discriminatory.”

Both opponents and supporters of network neutrality came to the same conclusion after the Court of Appeals hearing, that the judges are skeptical about the FCC’s arguments supporting the Open Internet Order. Combined with Wheeler’s endorsement of two-sided markets, it looks like ISPs and wireless carriers will have plenty of opportunity to make money both from individual consumers and the content companies trying to reach them.


ACL in traffic policy on Huawei device

10 Oct

Frame Relay Traffic Fragmentation | Complete Lab Included

12 Sep

Cisco’s Frame Relay Traffic Fragmentation scheme supports the transport of mixed voice and data traffic across the same interface without the longer data frames causing excessive delay to the normally smaller-sized voice packets. The longer frames are fragmented by the router into a sequence of shorted frames at the sending end. Then the smaller fragmented data frames can be interleaved with the real-time delay-sensitive voice frames.

In this lab, we’ll configure frame relay traffic fragmentation over the link between R1 and R2. Network administrator has determined that 150 bytes is the optimal transmission size over this line.

Frame Relay Traffic fragmentation


Network Diagram: Frame Relay Traffic Fragmentation

Trap: Frame relay traffic-shaping can be configured one-way, but fragmentation cannot. If one side is not configured for fragmentation, it will not know what to do with a fragmented frame and therefore will discard it.

We enable frame relay fragmentation on the link by configuring both routers with fragmentation commands.

Download the Qmap to get detailed configuration guide, commands, outputs and map of the lab. You will need NetBrain Qmap Reader to view this Qmap.

Download Qmap


Moqing quick reference (Cheat Sheet)

2 Sep


This post is just a quick reference for myself, when I’ve spent a while away from TDD and want to remember some of the tricks.

Initial construction

var mockFoo = new Mock<IFoo>(MockBehavior.Strict); 

Accessing the mock object as IFoo

IFoo foo = mockFoo.Object;

Initializing the inversion of control container (StructureMap)

Generally use Configure, not Initialize. Configure is additive, Initialize is not. Here’s a complete test method showing the different ways we typically initialize.

[TestMethod()]public void TestMethod()
   var mockOrange = new Mock<IOrange>(MockBehavior.Strict);
   MyDefaultHandlerForPear pearConcretion = new MyDefaultHandlerForPear();
   ObjectFactory.Configure( x=> 
      // use mock object
     // use new class when asked
     // use an existing instance that implements IPear 

Setting up void calls

public interface IOrange{  void Peel();

View original post 454 more words

Video adverts – glass half empty or half full for mobile operators?

25 Jun

As radio access technologies have evolved and downlink speeds have increased, we’ve seen video content taking up a larger proportion of the total data traffic on mobile networks. Specifically, as RANs have moved through 2G to EDGE, UMTS and into HSPA, the combination of faster networks, cheaper data, smarter devices and better QoS for video means that video as a percentage of traffic has risen from 30 percent to over 50 percent.

In the five years since the iPhone first launched and kick-started a smartphone revolution, video has become a much bigger portion of traffic on mobile networks. Unfortunately for mobile operators, the proportion of revenue that video brings in has not grown at the same rate as the traffic. As we move into LTE and towards LTE-Advanced we’re going to see the share of video continue to grow.
We may not need to wait long for video traffic to accelerate its growth. Facebook looks set to begin moving forward with video adverts, a step that may be setting off alarm bells at networks operators around the world – or at least wherever Facebook has a presence. In the Citrix Bytemobile Q2 2013Mobile Analytics Report we found mobile Facebook usage has grown five-fold in a year, with the level of traffic Facebook generates growing from one to five percent of overall data traffic. That includes very little video. So, what happens when we add video adverts to Facebook?
In the same Mobile Analytics Report, we indicate that a single mobile YouTube session contributes the equivalent of ten mobile Facebook sessions — that’s the power of video. When Facebook rolls out video adverts, there will be an unavoidable bump in data traffic associated with Facebook. When video adverts are added to all of the other online services, that new traffic could start to have a seriously detrimental impact on the quality of service subscribers enjoy.

Of course, even in the absence of video adverts, operators have had to manage the growth of data on their networks – or risk financial implosion. The tools used range from relatively blunt instruments such as throttling and lossy compression to more nuanced approaches such as adaptive traffic management, the latter employed in the service of ensuring the best possible subscriber QoE within the constraints of the network. More recently, operators have begun to consider QoE as part and parcel of a differentiated services plan in which the subscriber experience – including access to particular content – is tied directly to willingness to pay. The question at hand is: which of the above approaches – or other approaches — are most relevant in the context of video adverts?

There are a few things to consider. First, these adverts will be pushed over the network over and over again, making the excess traffic challenge that much worse. Second, at approximately 2MB each, these adverts, if seen many times per day and month, can serve to consume a significant portion of the subscriber’s quota. Finally, the effectiveness of the advert and the likelihood of having a subscriber click through will be related to the video experience, for example the amount of time it takes for the advert to load.
Given these considerations, it’s clear that some degree of data optimization is required in order to reduce the overall transport costs and, perhaps more importantly, to minimize the hit to subscriber quotas. Adverts can be easily cached and highly compressed, so the blunt instrument approach could certainly be justified. After all, the operator isn’t making any extra money from these adverts. But they could be.
If ever there were an opportunity to pursue a two-sided business model – one in which the operator collects revenue not just from subscribers but from content providers as well – this is it. Both Facebook and its advertisers have a financial incentive to generate as many click-throughs as possible. Meanwhile, the operator — in partnership with an optimization vendor — has the tools at its disposal to affect this click-through rate while better serving the interests of its subscribers.
At the most basic level, an improved user experience enabled by optimization would better ensure the timely delivery of the advertisement. Taken a step further, the operator could use its understanding of real-time user experience to only send adverts when the subscriber is likely to have a good experience. Who’s benefitting here? Facebook (or equivalent). The basis for an agreement between the operator and the social networking site is thus established.
And it doesn’t need to end there for either party. The operator has the option of zero-rating the traffic associated with these video adverts, so as not to unfairly consume the subscriber’s data quota. The presence of a business arrangement between the two makes doing so even more palatable. Perhaps of most interest (and most controversy) is the idea of sharing certain elements of the subscriber profile – elements inaccessible to Facebook or the advertiser – thus enabling the more effective targeting of adverts. This, of course, is exactly what all parties want.
What looks at first like a network burden that must be ignominiously accommodated like so many others is in fact an opportunity for operators to establish partnerships with the content providers, for whom an enhanced subscriber experience translates to revenue.
*Chris Koopmans joined Citrix as vice president, Service Provider Platforms, with the acquisition of Bytemobile in 2012. He is responsible for product development, management and marketing for the Service Provider Platforms group, as well as the Citrix business strategy for telecommunications service providers. Koopmans was a founding engineer at Bytemobile in 2000 and rose to the position of chief operating officer, responsible for all aspects of product development, management, marketing, delivery, and support, as well as information technology (IT). He has over 14 years of industry experience in hardware and software engineering and architecture.
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