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The global mobile Internet traffic is expected to grow 18-fold over by 2016, reaching 130 exabytes annually by 2016, according to Cisco’s Visual Networking Index Mobile Forecast, 2011-2016.


Now, what does this growth of mobile traffic mean for mobile operators? How will the changing mix of mobile devices and connections (such as smartphones, tablets and M2M) impact their networks, their businesses and strategic partnerships?

Accanto Systems has some suggestions as to how operators can manage the mobile data traffic explosion.

The introduction of LTE poses additional challenges to operators that are striving to ensure the best possible customer experience

. These challenges range from the continuous explosion of mobile data traffic and applications, to the management of another set of network elements.

Interfaces that overlay the existing infrastructure to the growing ecosystem of sophisticated end user devices like smartphones and tablets.
So much signaling and data traffic can now be collected, however, the main challenge is making sense of it all.

Top N and Peer analysis, according to Accanto, are useful tools to turn a mass amount of data into actionable intelligence.

Top N Analysis is the technique of focusing just on the most important elements so that the operator can devote its attention to the issues that have the most direct effect on the problem at hand.
For instance:  What are the worst performing network areas or devices for operations?
• What are the highest error causes for a particular failure?
• What are the most used services?
• What are the most capacity-overloaded areas?

Top N algorithms are designed so that it is efficient to identify these most important cases in real-time, rather than having to post-process vast amounts of information.

The approach is a proactive one, where the information monitored in real time across the network is used to understand where there is potential dissatisfaction and identify the root cause of the problem.

Accanto says Peer Analysis is a technique of comparing a particular measurement among a group of peers to provide additional context to the measurement and better root cause analysis.

For instance:

Is the quality this particular subscriber is receiving better, the same or worse than the quality of those in the same cell?

•Is the problem of a particular device common to other devices, or does it present itself just with this brand and model?
• Is a particular service not available to all customers, a subset of customers or just one customer?

In this case the approach is to start from the reactive analysis of a particular customer or group of customers that express dissatisfaction. And, use this information to understand whether this is a symptom of a widespread problem that may not have been identified as such.

By Angela Sutherland                          

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