PERFORMANCE ANALYSIS AND SYSTEM OPTIMIZATION OF AN ENERGY-SAVING MECHANISM IN CLOUD COMPUTING WITH CORRELATED TRAFFIC

Abstract

Energy consumption is becoming a significant part of overall operational cost in cloud data centers. For the purpose of satisfying the Service Level Agreement (SLA) of cloud users while enhancing the energy efficiency in cloud computing systems, in this paper we propose an energy-saving mechanism with a sleep mode. Taking into consideration the traffic’s correlation and the stochastical behavior of data arrival requests in a random cloud environment with the proposed energy-saving mechanism, we model the system as a MAP/M/N/N+K queue with a synchronous multi-vacation. Then, we present a theoretical basis for analyzing and evaluating the system performance by taking a state transition rate matrix in the steady state. Next, we investigate the change trends for the energy saving rate of the system and the average latency of tasks by carrying out numerical experiments. Moreover, we give a cost function to balance the system performance measures and optimize the performance of the system with the proposed energy-saving mechanism through intelligently utilizing the Chebyshev chaotic map and enriching the biological behavior of fireflies in an improved Firefly Algorithm (FA). Finally, we obtain the optimal sleep parameter and the minimal system cost using the improved FA.