Proactive multipath routing with a predictive mechanism in software-defined networks

Abstract

With the growth of network traffic volume, link congestion cannot be avoided efficiently with conventional routing protocols. By utilizing the single shortest-path routing algorithm from link state advertisement information, standard routing protocols lack of global awareness and are difficult to be modified in a traditional network environment. Recently, software-defined network (SDN) provided innovative architecture for researchers to program their own network protocols. With SDN, we can divert heavy traffic to multiple paths in order to resolve link congestion. Furthermore, certain network traffics come in periodic fashion such as peak hours at working days so that we can leverage forecasting for resource management to improve its performance. In this paper, we propose a proactive multipath routing with a predictive mechanism (PMRP) to achieve high-performance congestion resolution. PMRP has two main concepts: (a) a proactive mechanism where PMRP deploys M/M/1 queue and traffic statistics to simulate weighted delay for possible combinations of multipaths placement of all subnet pairs, and leverage genetic algorithm for accelerating selection of optimized solution, and (b) a predictive mechanism whereby PMRP uses exponential smoothing for demand traffic volumes and variance predictions. Experimental results show a 49% reduction in average delay as compared with single shortest routing, and a 16% reduction in average delay compared with utilization & topology-aware multipath routing (UTAMP). With the predictive mechanism, PMRP can decrease an additional 20% average delay. Furthermore, PMRP reduces 93% of flow table usage on average as compared with UTAMP.