An energy-aware method for data replication in the cloud environments using a Tabu search and particle swarm optimization algorithm

Abstract

Cloud computing is a type of parallel, configurable, and flexible system, which refers to the provision of applications on virtual data centers. However, reducing the energy consumption and also maintaining high computation capacity have become timely and important challenges. The concept of replication is used to face these challenges. By increasing the number of data replicas, the energy consumption, the performance, and also the cost of creating and maintaining new replicas also are increased. Deciding on the number of required replicas and their location on the cloud system is an NP-hard problem. In this paper, the problem is formulated as an optimization problem and a hybrid metaheuristic algorithm is offered to solve it. The algorithm uses the global search capability of the Particle Swarm Optimization (PSO) algorithm and the local search capability of the Tabu Search (TS) to get high-quality solutions. The efficiency of the method is shown by comparing it with simple PSO, TS, and Ant Colony Optimization (ACO) algorithm on different test cases. The obtained results indicate that the method outperforms all of them in terms of consumed energy and cost.