Energy-Efficient Resource Allocation and Provisioning Framework for Cloud Data Centers

Abstract

Energy efficiency has recently become a major issue in large data centers due to financial and environmental concerns. This paper proposes an integrated energy-aware resource provisioning framework for cloud data centers. The proposed framework: i) predicts the number of virtual machine (VM) requests, to be arriving at cloud data centers in the near future, along with the amount of CPU and memory resources associated with each of these requests, ii) provides accurate estimations of the number of physical machines (PMs) that cloud data centers need in order to serve their clients, and iii) reduces energy consumption of cloud data centers by putting to sleep unneeded PMs. Our framework is evaluated using real Google traces collected over a 29-day period from a Google cluster containing over 12,500 PMs. These evaluations show that our proposed energy-aware resource provisioning framework makes substantial energy savings.