Profile-Based Static Virtual Machine Placement for Energy-Efficient Data center

The energy consumption of a data center and hence the carbon footprint from it largely depends on the energy consumption by its active Physical Machines (PMs). Researchers have taken many attempts to minimize the data center energy consumption through the Virtual Machines (VMs) allocation into a minimal number of PMs of homogeneous types. However, the current VM placement strategies do not consider useful information that can be extracted from data logs of data center. This paper presents profile-based VM placement approach to improve energy efficiency of data centers. The approach formulates the energy consumption problem as a profile-based optimization problem. Then, the problem decomposed into multiple smaller ones in a number of intervals. For each intervals, a number VMs and PMs are sorted in terms of resource requirements and energy efficiency respectively. Then, the First Fit-Decreasing(FFD) is adopted to place the sorted VMs to the sorted PMs. Experiments conducted to demonstrate the presented approach with comparisons with the original FFD algorithm. The experimental results have shown that the presented approach can reduce more energy consumption than the original FFD algorithm and is scalable for larger test problems.