Mobile data networks are experiencing unprecedented amounts of data flow, and the resulting congestion is seriously beginning to threaten their performance and ability to service their subscribers. With more smart devices in service – everything from wireless laptops and netbooks, to iPhones and Google’s Android-based phones – coupled with more voracious user appetites for mobile access to online information, it’s no wonder there is greater congestion, dropped calls, slower connection speeds and increasingly dissatisfied subscribers.
In Europe and Asia, where landline service is expensive, subscribers are adopting mobile broadband in mass, challenging operators’ existing infrastructures. Here in the U.S., smartphones are creating problems for our infrastructure. iPhone users, for example, are generating a 10 times increase in data flows. Overloaded networks are even impacting sales of mobile devices. In fact, in New York City, AT&T had to suspend iPhone sales in December. While specific reasons have not been given, it’s been widely suggested that it was because of overloaded networks.
Operators are in dire need of solutions to help manage the insatiable need for mobile data as well as the huge growth that threatens to choke mobile networks.
Currently, this congestion is being countered with a number of strategies. However, traditional methods for increasing capacity are becoming harder to implement. More spectrum in many geographies is not available; increasing cell site density is very expensive and upgrading hardware can be slow. New methods, such as femtocells and Wi-Fi offload, are emerging to help route data traffic to other networks. Additionally, policy-based tariff plans will materialize to differentiate subscribers, services and traffic loads. All these solutions may prove to address the issues; but it will take time for these deployments and business models to solidify.
There is also a host of content adaptation and optimization solutions, including compression technologies, transcoding, video optimization and caching, which are being deployed to address network congestion. These are often very useful but they are not without their limitations and challenges. With more USB modems connecting to laptops, higher resolution smartphone displays and content providers compressing or manipulating more content at the source, compression and transcoding solutions offer fewer benefits. Furthermore, these solutions can create inefficiencies that limit network performance and degrade subscribers’ experiences. For example, compression can decrease image and video fidelity, creating a down-level experience which may upset and confuse subscribers using laptops and USB modems.
More challenging for operators is the lack of compatibility that some optimization solutions can introduce into a network. Optimization solutions that manipulate packet payloads create “tunneled” connections that can often disrupt the implementation of deep packet inspection (DPI) and policy strategies. For example, a DPI solution that has been deployed to differentiate and manage network traffic may not be able to process some compressed or “tunneled” connections. There are certainly “workarounds” to manage some of these challenges; but these illustrate the growing complexity and interdependencies of deploying different technologies to address congestion.
AN OVERLOOKED OPPORTUNITY
A strategy that is often not considered but offers options for managing network resources is the deployment of more network intelligence at the edge. Network-edge intelligence is software at “the edge” – meaning on laptops and handsets. Leveraging the device’s processing power and location on the network can offer powerful tools to increase network efficiency and management. The benefits can range from better utilization of wireless resources, to enhanced policy implementation and increased knowledge of network conditions. Additional benefits include more powerful user equipment and operator-delivered laptop connection manager software, which facilitate deployment of these solutions.
An example of a benefit of network-edge intelligence is transport layer optimization. By deploying network software on laptops, transparent and additive optimization can be introduced that work in concert with existing protocols and technologies while significantly increasing the efficiency of networks.
Transport layer optimization has the ability to address the inefficiencies that transmission control protocol (TCP) introduces on wireless networks. It can accelerate data sessions, which would speed data to subscribers and free up network resources. These resources can then be used for other subscribers or data connections. It’s a solution that will help with congestion by increasing utilization. And, perhaps more importantly, the solution can be deployed as a truly transparent, layer-4 implementation – which means it will complement other optimization solutions and provide additive increases in efficiency. It also allows for interworking with network intelligence solutions such as DPI and policy. The net result is a complementary and additive solution for managing congested networks that can be deployed quickly with no negative subscriber impact.
Optimization is only one example of leveraging intelligence at the network edge. There is much more that can be achieved, including enhancing DPI systems, more efficient policy enforcement and the collection of additional network information. Problems with network congestion will only increase, and operators around the world are acknowledging the opportunity to leverage software at the edge to address this congestion. Operators both in the U.S. and abroad are beginning to see the huge advantage of turning the very devices that are creating the problem into a part of the solution.
Chris Hill is a vice president of marketing for Mobidia Technology.