Daniel Joseph Barry, Napatech: Avoiding the Scissor effect intelligently
February 2011 by
Avoiding the dreaded Scissor Effect has become the number 1 priority for mobile carriers. The scissor effect refers to the phenomenon of rising infrastructure costs and flat revenues; an unsustainable situation for any business. The scissor effect has already been witnessed in fixed line networks and now mobile carriers face the same challenge in relation to mobile data services. Is it possible for mobile carriers to grow revenue per user in line with bandwidth usage?
The key to avoiding the scissor effect is intelligence. You can’t manage what you can’t see, so more intelligence on network and service usage is a fundamental first step. But, intelligence can also be understood as providing smarter services that better meet the needs and expectations of customers and, for which, mobile carriers can earn revenue in line with bandwidth growth. The intelligence gathered from the network lays the foundation for building more intelligent services, which in turn leads to more satisfied customers, who spend more.
To understand how the scissor effect can be avoided, we need to understand how it can materialize. In the case of fixed-line broadband services, the scissor effect was driven by two factors: an uncertainty and lack of confidence in the potential uptake of broadband services followed by a high growth rate in bandwidth usage.
Telecom has been (and probably always will be) driven by technology. High-speed Internet access via Digital Subscriber Line (DSL) technology was available long before carriers were sure if there was a market for broadband Internet access. There was therefore a desire to make broadband Internet access services attractive and to keep things simple. Hence, flat rate fees and all-you-can-eat models. The ensuing Internet traffic growth of 100% per year quickly introduced problems as each subscriber used more bandwidth, but was not paying more for this usage.
Unfortunately, the genie was out of the bottle. Once consumers were used to flat-rate fees and all-you-can-eat models for Internet access, there was no turning back. Competing with incumbent carriers meant offering the same service for less at flat rates, even as Internet traffic continued to grow and infrastructure investments were needed to keep up.
The subsequent introduction of Youtube, Facebook and other bandwidth hungry web services only exaggerated the issue.
Mobile carriers have had a ring-side seat to witness these developments and yet, have almost fallen into the same trap with the introduction of mobile data services. Again, confidence in the uptake of mobile data services was probably not high and flat-rate fees were again offered. The growth in mobile data take-up has been staggering, to say the least and the prediction is for a doubling of mobile data traffic each year for the next 5 years. Sound familiar?
Mobile carriers are, however, fully aware of the threat and have taken steps to respond with various solutions based on Deep Packet Inspection to manage traffic. This includes services where consumption caps are introduced (i.e. you pay a flat-rate up to a certain download limit and higher rates thereafter) and even degradation of performance for “undesirable” services, such as peer-to-peer downloads.
These approaches are effective, but are they customer-friendly? Will this approach lead to more satisfied customers who are willing to pay more or customers ready to switch provider as soon as the option arises? How easy will it be for a hungry competitor to compete with this model? I think the answers are clear.
An alternative approach is to build a strategy based on understanding and satisfying customer needs and providing services that reflect how they would like to use their mobile data services. The proposition is that by concentrating on providing exactly what customers want, they are less likely to switch provider and are more likely to pay more for the convenience and value their mobile data services provide.
The key to achieving this is intelligence. The first step is gathering intelligence on network and service usage, so we understand how customers are using their mobile data services and that they are receiving the quality of experience they require. With this intelligence, it is possible to tailor services to different types of customer usage scenarios. For example, some customers are more active during the day, others in the evening. Some customers are more active on Facebook, others more interested in news broadcasts or music download. With this intelligence, a better basis for network planning is established, based on a much better understanding of how and when customers will use their services. The infrastructure established to gather network and service intelligence data can also be used to monitor usage in real-time trends and shifts in behavior that can be detected early allowing changes to network planning and service plans to be made, not to mention pricing models. In short, more intelligence on network and service usage leads to more intelligent, agile and responsive service definition, pricing and network planning. What is required is the establishment of a network intelligence infrastructure that can provide the data, in real-time, that is required to make this a reality.
This investment need not be expensive. It is possible to build Deep Packet Inspection and Policy Server systems using off-the-shelf standard server hardware and commercial intelligent network adapters. This provides an extremely cost-effective hardware platform with high-performance. Since multiple systems will need to be deployed at critical locations in the network, it is important to base development on a cost-effective, high-performance, reliable and, most importantly, scalable platform.
Scalability is absolutely essential as mobile data traffic threatens to swamp mobile networks. The advantage of standard servers is that the underlying server chipsets are increasing performance by up to 60% each year. What’s more, these chipsets are based on multiple cores with higher densities available on an annual basis. The availability of more and faster processing cores each year provides an opportunity to scale performance as and when new standard servers are available.
In short, to build intelligent services, you need network intelligence based on systems that are built intelligently.