By
Yehuda Elmaliach*, CTO & Co-founder,
Mobixell
Back in 2002, when 3G implementation started taking off, mobile operators enthusiastically made the jump from voice and messaging to data services, expecting exciting new content and services to become the new cash cow. Commodity voice and messaging revenues dropped steadily, as expected and data traffic, and revenues, increased. The years passed and the cow started getting fat. Mobile networks, powered by 3G technology, were supposed to swing the gates wide open to the flow of rich Internet content. But as animations turned into video clips and clips became 24/7 streaming movies and TV shows, those gates started to feel narrow. As we approach 3G’s 10th anniversary, history seems to be repeating itself.
4G/LTE is the new kid on the block promising to widen those old 3G gates with superfast mobile Internet access. The problem with more speed is more fun. And that means more congestion as new and more popular content becomes available and new mobile devices replace slower and less capable devices. Increasing mobile data traffic is inevitable, even welcome: more data equals more revenue. But that doesn’t mean that huge increases in traffic need to clog up mobile networks or that huge investment in network expansions will force operators to sacrifice profitability.
For now, mobile data optimization is successfully helping mobile operators to manage their investment in network infrastructure. But is data optimization an interim fix until the almighty 4G/LTE comes along? Or is it just another case of history repeating itself (as was the case just before the then-almighty 3G came along). Recent traffic patterns have shown that enlarging the pipe leads to more consumption and data-hungry applications, which in turn will lead to higher traffic demand. Nielson’s law states that “Network connection speeds for high-end users would increase 50% per year, or double every 21 months” – LTE is the right direction, but it’s definitely not a cure-all solution and data optimization will still be required to mitigate the growth vs. the expenses.
However, over the next few years as mobile traffic expands (to what extent is still a point of contention), optimizing the enormous volume of data will become a heavy load in both hardware to support the processing but more importantly, in cost. And as traffic levels go up, so will the cost of optimization for the operator. But can this link be broken? It must be, and it can be.
As long as operators expand their optimization solutions to match their data volume, the cost of optimization will approach the unsustainable levels of the traffic itself. To break this link between soaring traffic volume and the cost of optimization, optimization technologies need to ignore most of the traffic and focus only on the traffic that causes congestion – the traffic that effects the subscriber’s mobile broadband experience. By predicting, through real-time traffic analysis, when and where congestion is about to happen and then applying optimization techniques only when absolutely needed, mobile data optimization takes on an evolved approach.
Evolved mobile data optimization needs to go beyond data volume reduction and even congestion detection. Even when an optimization solution performs offline statistical analysis of cells and aggregation links to predict congestion, the accuracy is limited by the method since congestion can present itself anywhere and at any time. With statistical congestion detection, optimization resources will still be wasted on streams that do not need to be optimized and subscribers will still experience congestion where and when offline statistics cannot predict transient spikes. By proactively performing real-time near-congestion prediction on a stream-by-stream basis, an evolved approach to optimization can dynamically apply the most appropriate optimization techniques when needed to avoid congestion before it affects the subscriber’s quality of experience.
Another element of evolved optimization is cloud-based caching and optimization. Since mobile network operators are already looking into cloud-based virtualization technology to support various services, using a similar model for optimization resources seems like an obvious step toward more cost effective and efficient mobile data optimization. Either through a private cloud-based model or sharing outsourced resources for even more efficiency, a mobile operator can significantly limit investment in optimization, even as the need for optimization grows with soaring data traffic levels.
These methods – real-time near-congestion prediction and cloud-based caching and optimization – are just a few ways that mobile operators will soon be able to continue to provide quality service while sustaining profitability long into the future. Once operators can better focus on the basics of providing high-quality services, they can then more easily adopt new and innovative ways of generating revenues that take advantage of their reliable, available network resources. If operators choose to learn from history, they will be able to change their future.
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Yehuda is the co-founder of Mobixell, and previously co-founded Optibase Ltd. (NASDAQ:OBAS),a leading specialist in the delivery of MPEG-based digital video over ATM and IP-based networks. During his years at Optibase, Yehuda held a number of different managerial positions including Vice President of Research and Development, and Chief Technology Officer. Prior to this, he worked at Intel's design center in Israel.
Yehuda holds a BSEE from the Technion (Israel's Institute of Technology).
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