Power efficient resource allocation in cloud computing data centers using multi-objective genetic algorithms and simulated annealing

In this paper we present a new task allocator for Cloud Data Center (DC). The implementation is based on two different heuristics: Multi-Objective Genetic Algorithms (MOGA) and Simulated Annealing (SA). The allocator reduces at the same time both task completion time and server and switches power consumption, avoiding network link congestion. The evaluation results show that the developed approach is able to perform the static allocation of a large number of independent tasks on homogeneous single-core servers with a quadratic time complexity for MOGA and a linear time complexity for SA.