Operational Support Systems (OSS) are used by communication service providers to perform network planning, network provisioning, service fulfillment, and service assurance. In addition to improving the efficiency of planning and operations teams, OSS contribute significant financial benefits to a service provider’s overall CAPEX and OPEX network equipment budgets.
With the growth of broadband mobile services, the management of numerous types of data streams over a common IP based network has become critical, requiring the ability to accurately monitor network performance and usage in real time to predict where degradation of the service may occur based on constant evaluations of network data.
It is not only in the off-line analysis of services but also in the execution and operation of new services that the mobile operator business is becoming data driven. The mobile operators’ opportunity and challenge is to combine processes for analysis of “Big Data” coming from the dynamics between all sources with an agile ability to create data driven services.
The movement into big data driven business evolves around using the data collected from the network in a combined approach for both analysis and service creation. Rather than using data to investigate what has worked (or not worked) in the past, data has now become a platform for both how to market different services and as an enabler for these new services. Examples of such services enabled by this approach include Location Based Services (LBS) or fair allocation of scare network resources, e.g. mobile radio access resources. This movement into real-time capable platforms poses challenges to traditional centralized software centric approaches because the growth in data by far supersedes the evolution in server processing capabilities.
The real-time distribution of GTP traffic poses a problem in that the data collecting probes used for service monitoring and troubleshooting typically have a lower processing capacity than the traffic. Furthermore, these probes must have all traffic belonging to a certain handset/subscriber to deliver reliable data. Traffic distribution using traditional splitting technologies is not possible because the traffic flows in the network are load shared over several physical connections. A realization of the needed distribution of GTP traffic in software is inefficient due to the ever increasing data loads in mobile networks.
The solution is to move as much as possible of the data processing from processor cycles to hardware accelerated platform elements. The hardware accelerated platform used must maintain a high level of flexibility to keep up with rapid changes. The Silicom Denmark approach revolves around the usage of FPGAs that sit very close to both the interfaces from which data must be captured and the communication bus available in a hosting server. This is done using a layered move of data processing intelligence from processor cycles to hardware.
Silicom Denmark enables service providers to collect, correlate, and analyze network data with highly scalable network monitoring solutions and allows them to compete at lower cost in the next generation of communications and entertainment services.