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.
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.