risk of congestion
It’s 7AM and I can’t put off getting up any longer, so I look out the window and see there’s a light frost on the grass, which the weather channel warned me about 3 days ago. An hour later I’m at the train station waiting for the 7:42 which is delayed because the frost has caused the points to seize up in a town 50 miles away, so now the entire South East rail system is completely snarled up. They predicted the weather and probably knew there would be a point’s failure, but still the network crashed. So I need to phone work to let them know I’m going to be late, but I can’t connect. Thousands of other commuters around me are also trying to phone ahead, but the network can’t handle it. It seems to happen every day.
We are surrounded by events which are beyond our control, but often they happen in predictable ways. The points failure was perhaps less predictable than my alarm, but we always knew that when the temperature dropped there would be some kind of failure somewhere that would lead to cancellations and a breakdown of the network. We always knew that rush hour would become an agonising crawl into town on overcrowded trains. The congestion could probably have been avoided if they could have predicted which parts of the network were under the most stress and the impact on the network in the event of failure or congestion at those stress points. Additional resources could then be provided at those points, or alternative routes planned to bypass the congestion and limit the ripple out effect, like a fire break. The problem is only likely to get worse, and the network more unreliable, as the population increases and more people than ever rely on the rail network to get to work.
With the arrival of LTE and rapidly increasing popularity of Video on Demand then telecoms networks are also facing increasing levels of congestion and instability. Global data traffic is predicted to increase by 10 to 20 times by 2019 (Cisco). In order to meet regulatory obligations and maintain customer experience Capex is set to spiral upwards. MNOs, who are already facing a year on year decrease in ARPU, will struggle to keep pace with demand and the risk of congestion will be ever present.
As with rail networks MNOs need a longer term strategy in place to understand where and when future choke points in the network will occur so that the risk of congestion can be eliminated for the least cost. Subex Capacity Management provides the capability to predict these points of congestion by monitoring and correlating metrics from across the network to provide detailed forecasts of network utilisation. Additional factors can be brought into the forecasts, such as the impact of major events or the rolling out of M2M services and different scenarios played out to understand how the network will respond. By automating the forecasting process network managers can be alerted long before issues become critical and congestion begins to occur. They can evaluate different options for either re-homing traffic or augmenting the network for the least possible cost. Stranded or un-utilised assets can even be recovered and re-located to satisfy demand for very little cost.
CFOs need to find ways to keep increasing revenue while controlling costs, and CTOs need to keep network delivering ever greater speeds as volumes of traffic increase exponentially. Both need to look into the future to avoid a future of network instability, falling quality, crippling network costs and lost revenue.
Mark Jenkins has worked in the IT industry for over 15 years as a BI and Analytics consultant, and more recently as ROC Product Manager for Subex Ltd. He has designed and deployed solutions for global companies in many sectors including Insurance, utilities and telecommunications. Mark holds a BSc Hons in Computer Science from Manchester University (UK).