Home North AmericaNorth America 2010 Adaptive content optimization in the RAN

Adaptive content optimization in the RAN

by david.nunes
Scott HiltonIssue:North America 2010
Article no.:14
Topic:Adaptive content optimization in the RAN
Author:Scott Hilton
Title:Vice President and General Manager, Broadband Optimization Solutions
Organisation:Sycamore Networks Inc.
PDF size:161KB

About author

Scott Hilton is the Vice President and General Manager of the Broadband Optimization Solutions business unit at Sycamore Networks. Before joining Sycamore, Mr. Hilton was Vice President of Product Marketing and Management at 3Com Corporation, and prior to 3Com, Vice President of Product Management for the IP Services division of Lucent Technologies. Scott Hilton completed a B.S. in Electrical Engineering from Duke University and an M.S. in Electrical Engineering from George Mason University.

Article abstract

Mobile operators across the globe are struggling with network congestion caused by the unprecedented uptake of mobile broadband. As well as baseline traffic, high demand traffic peaks have increased significantly and can have a major impact on the radio access network. Adaptive content optimization is a compelling solution, providing an effective mechanism for relieving traffic congestion and managing the growth of mobile broadband traffic.

Full Article

In the first half of 2010, the average US mobile subscriber consumed an average of 230 MB of data per month, a rise of 50 percent over the previous six months. The increased use of smartphones and mobile broadband has clearly driven this growth, with 31 percent of the US subscriber base now classified as smartphone users. As improved multimedia functionality and capabilities are added to successive generations of mobile devices, the stress on existing 3G networks is only set to increase. According to a recent statement from Verizon Wireless, their recently launched Droid X phones use approximately five times the data volume of any other device including the iPhone. Proof that when consumers have a device that works well for accessing the Internet, watching video and other online activities such as social networking, they will use it to full effect. Operators are at the forefront of this revolution in mobile data and are struggling with competing forces that will determine the long-term sustainability of their business models. Mobile broadband is their main growth engine today, especially in mature markets. But as subscribers flock to mobile broadband services and impose increasing bandwidth demands on the network, costs are escalating much faster than revenues. Operators are faced with a tough choice: continue to add capacity to alleviate congestion and meet customer experience expectations, or, pay dearly later to combat high customer churn and market share erosion. The impact of this increased data usage – more congested networks – is already starting to show and will have an effect upon users everywhere. Caps on 3G data allowances have already been introduced by Sprint and more recently AT&T, Verizon has already hinted at data caps for their LTE network, and other operators are expected to roll out similar schemes. Unlimited 3G data download packages may soon be relegated to history. As capacity and performance pressures build on the network, business and consumer users who rely on mobile broadband are likely to experience further restrictions on use, inconsistent Internet connections and slow downloads or delayed interaction. The mobile broadband dilemma and RAN congestion Nowhere is the mobile broadband dilemma more challenging, or congestion more acute, than in the cost-sensitive Radio Access Network (RAN), which provides connectivity between the radio base stations (Node Bs in High Speed Packet Access (HSPA) networks) and Radio Network Controller (RNC) hub sites aggregating and processing mobile traffic. As Internet video drives exponential traffic growth and rapidly changing traffic patterns complicate network dimensioning rules, mobile operators face a number of critical network issues in the RAN that impact both capital and operational expenses. These include: • The need to rapidly upgrade backhaul connection speeds to avoid traffic bottlenecks; • The timing of network equipment upgrades and investment; • The fact that capacity planning is becoming increasingly reactive; and • The impact of poor network performance on customer satisfaction and churn. The ideal solution for this high-cost portion of the network must address the service delivery economics while adapting to location, network loads, and the traffic mix. Backhaul capacity upgrades: Is this enough? One clear choice for mobile operators to solve the congestion crunch in this portion of the network is to invest in upgrading to faster (higher bandwidth) backhaul connections. The viable options tend to fall into three major categories: • Add capacity to the existing backhaul network – this is often the quickest and easiest to accomplish operationally. However, it can also be the most expensive as it does not fundamentally change the economics. An example of this approach is to add additional T1/EI leased lines or TDM microwave capacity to a cell site. • Upgrade to packet-based backhaul – this approach requires significant planning, time, and capital investment. However, it can provide a significant leap in capacity while lowering the recurring operational cost per Mbps of incremental bandwidth. Examples of this approach include upgrading from T1/E1 copper circuits to fiber fed, Carrier Ethernet services or upgrading from TDM-based microwave to Packet/Ethernet-based microwave. Both of these approaches require major network, equipment, right-of-way agreements, spectrum leasing, and operational upgrades that can incur significant time and cost. • Offload and divert certain backhaul traffic – this approach offloads mobile data traffic to lower cost transmission alternatives. Examples of this approach include xDSL offload at the base station and the use of femtocells and Wi-Fi hotspots that can divert the data off the macro network onto cheaper fixed broadband connections. These approaches can be effective but have economic, operational, spectrum planning, policy and SLA implications. While these options can be effective in a number of cases, they are typically expensive, reactive, and do not fundamentally solve the underlying challenges: exponential growth of mobile data services; the cost and time to deploy new transport; and the unpredictable nature of when and where congestion will occur. The operational impact of high demand mobile broadband peaks In the course of conducting traffic studies with a variety of network operators around the world, Sycamore has accumulated a range of interesting data that sheds light on the evolving characteristics of mobile broadband traffic and the underlying impact of different content types (mix) and usage patterns. Typical content types include video, images, text, and other binary data that are transported through a variety of protocols between users and servers. The increase in smartphone usage has driven a dramatic increase in the number of data sessions in mobile networks, with greater than ten times the normal number of session attempts relative to voice-only handsets. While these trends have clearly increased baseline traffic, what is often overlooked is the network impact of high demand traffic peaks, which have increased significantly. Most of the congestion issues in the RAN, in fact, tend to be short-term or transitory, such as spikes in traffic demand during peak hour periods or unpredictable bandwidth surges caused by flash events. High demand consumption peaks can easily be four to five times the baseline traffic, and are most frequently driven by applications such as Internet video and bandwidth-intensive downloads of software updates. It becomes cost prohibitive, not to mention problematic, for a mobile operator to size their network (i.e., add capacity) to accommodate this type of peak traffic since it is not easy to predict where or when those peaks will occur. And while operators will continue to utilize bandwidth expansion options to address points of congestion in their network, this alone won’t solve the quality of service issues that high demand consumption peaks cause. For these reasons there is growing interest in technologies that enable operators to reduce the traffic loads traversing the backhaul and adapt in real-time – like a shock absorber – to the dynamics of peak traffic bursts. Targeting RAN congestion with adaptive content optimization Adaptive content optimization represents a different approach to helping operators manage the growth of mobile broadband traffic and its attendant operational expense in the cost-sensitive RAN. Unlike mobile core-based optimization schemes and proxy web caches, which primarily address upstream and interconnection bandwidth to the Internet, adaptive content optimization can dramatically lower capacity requirements specifically between the Node B and RNC. In this part of the network, even modest bandwidth (cost) savings can have a disproportionately positive impact on profitability. Adaptive content optimization reduces the traffic volume traversing the RAN by examining user content flows (including all traffic types – video, images, text, P2P) and applying advanced lossless data optimization techniques and adaptive learning algorithms in real time. This approach aims to reduce peak bandwidth requirements in the backhaul network and improve subscribers’ quality of experience, providing operators with a way to re-balance their service delivery economics in line with revenue growth – not traffic growth. Adaptive content optimization also provides operators with three important ways to optimize current network investments while easing the transition to the next generation of infrastructure. First, it addresses the main drivers of mobile broadband: IP video and Internet content – including P2P. Second, it is architected for all-IP HSPA radio networks, so it is a natural fit with LTE. Third, the technology is scalable to support LTE as a network overlay (the way most HSPA operators will deploy LTE), so existing HSPA sites can be efficiently combined with LTE radio deployments as they are rolled out. Conclusion Mobile operators across the globe are experiencing unprecedented uptake for mobile broadband services but struggling with the resulting network congestion and increasing costs that come with this success. For US mobile operators in particular, given the large populations they must serve and broad geographies their networks must cover, the challenge will be to continue upgrading their networks while delivering a good user experience and managing service delivery costs per MB in line with revenue growth rather than traffic growth. Adaptive content optimization is a compelling way to help operators relieve traffic congestion and thereby improve the user experience, delay infrastructure growth, and improve their service delivery economics.

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