The Prediction Analysis of Cellular Radio Access Network Traffic From Entropy Theory to Networking Practice
Traffic modeling and prediction are at the heart of the evaluation of the performance of telecommunications networks and attract much attention. The Prediction Analysis of Cellular Radio Access Network Traffic From Entropy Theory to Networking Practice Yet, conventional research of traffic prediction, although an established field, The Prediction Analysis of Cellular Radio Access Network Traffic From Entropy Theory to Networking Practice has been mostly concentrated on traditional wired broadband networks and rarely sheds light on cellular radio access networks CRANs . But the situation needs to be changed as the popularity of mobile devices e.g. iPhone and applications Facebook on them makes the traffic in CRANs shift from being voice-centric to datacentric . The Prediction Analysis of Cellular Radio Access Network Traffic From Entropy Theory to Networking Practice Meanwhile, the rebuilding of a traffic- aware energy-efficient architecture for cellular networks is becoming a trend. However, since CRANs have more stringent constraints on radio resources , relatively expensive billing polices and different user behaviors due to mobility and thus exhibit distinct traffic characteristics, research results from wired broadband network traffic cannot be directly applied to CRANs. Therefore, The Prediction Analysis of Cellular Radio Access Network Traffic From Entropy
Theory to Networking Practice motivated by incorporating traffic variations into the future cellular network design, this article attempts to study and predict the traffic dynamics in CRANs and provide certain guidance over how to apply the predicted traffic to the design of future CRAN architecture. Recently, tools from information theory have been introduced in various prediction scenarios such as atmosphere or climate and given a considerable number of intuitive conclusions. The basic idea is that entropy offers a precise definition of the informational content of predictions by the corresponding probability distribution functions (PDFs), The Prediction Analysis of Cellular Radio Access Network Traffic From Entropy Theory to Networking Practice and it possesses good generality because it makes minimal assumption on the model of the studied scenario. The entropy approach is therefore suitable for gauging the traffic predictability based on certain prior information from history or from neighboring cells.
The Prediction Analysis of Cellular Radio Access Network Traffic From Entropy Theory to Networking Practices
In this article, with the help of real traffic records of roughly base stations BSs in one month from China Mobile, we use entropy theory to understand the contributions of temporal and spatial dimensions and the inter-service relationship to traffic prediction in CRANs and provide some conclusions. Further, we describe some practical prediction means and present the relevant performance. After validating the traffic predictability, this article proceeds to address the practical applications of the predicted traffic. Nowadays, as the core network architecture is evolving toward software-defined networks SDNs, the predicted traffic could significantly contribute to network management in this future architecture. In SDNs, a control plane, which makes traffic routing decisions, is separated from the underlying data plane, which takes charge of traffic forwarding. Meanwhile, SDNs provide various open application interfaces (APIs) to external application engines e.g. access control , and thus facilitate network programmability.
As a result, the configuration process in SDNs can become moreflexible and scalable. Besides, traffic knowledgecan be exploited in an easier manner, so as to optimize routing policies and avoid congested routers. Inspired by this principle and methodology of traffic-aware SDNs, we later provide a In order to smoothly perform prediction analysis, this article collects the anonymous traffic records of nine mobile switching centers MSCs and serving GPRS switching nodes SGSNs with BSs. The collected dataset includes all calls, short message service SMS, and data logs in both rural and urban areas of around , serving about three million subscribers. The duration of the dataset spans from March to April .