Adaptive Strategies of Multi-Objective Optimization for Greener Networks

Increasing energy costs and environmental issues related to the Internet and wired networks continue to be a major concern. Energy-efficient or power-aware networks continue to gain interest in the research community. Existing energy reduction approaches do not fully address all aspects of the problem. We consider the problem of reducing energy by turning off network links, while achieving acceptable load balance, by adjusting link weights. In this research, we optimize two objectives, which are minimizing network energy consumption by maximizing utilization of shortest paths, and at the same time achieving load-balance by minimizing network Maximum Link Utilization (MLU). Increasing utilization of shortest paths provides the opportunity to switch off nodes and links, thus saving network power. This research proposes a new approach that relies on live data collected from wired networks, and performs Multi Objective Optimization (MOO) using a Non-dominated Sorting Genetic Algorithm (NSGA-II) that applies alternative adaptive strategies in order to optimize both objectives. Research to date has focused on the link level or traffic load balance, to minimize energy consumption, while putting less focus on utilizing adaptive strategic techniques that optimize multi objectives problems. This work proposes a novel approach to select underutilized links to go to sleep using adaptive strategies of MOO that are aware of traffic changes. Re-computing the algorithm should take less than a minute, while network traffic is frequently updated every few minutes. The hybrid approach we proposed was able to reduce the power consumption by 35%, while reducing MLU by 31% for specific traffic pattern used in Abilene network topology.