An experience-based scheme for energy-SLA balance in cloud data centers

Abstract

The proliferation of cloud computing has resulted in the establishment of large-scale data centers containing thousands of computing nodes and consuming enormous amounts of electrical energy. However, the low-cost and high-efficiency slogans are getting louder and louder, and the IT industry is also striving for this pursuit. Therefore, it is vital to minimizing the energy consumption for cloud providers while ensuring the quality of service for cloud users. In this paper, we propose several heuristic strategies to optimize these two metrics based on a two-level management model under a heterogeneous cloud environment. First, to detect whether a physical node is continuously overloaded, we propose an empirical forecast algorithm, which predicts the future state of the host by statistically analyzing the historical utilization data of the host. Second, we propose a weighted priority virtual machine (VM) selection algorithm. For each VM on the overloaded host, we weight several utilization factors and calculate its migration priority. Then, we simulate the proposed approach and compare it with the existing overloaded hosts detection algorithms with different VM selection policies under different workloads.