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 the fluctuations of resource requirements of a VM through its lifetime. To resolve the this issue, this paper introduces a novelty of profile-based VM assignment algorithm for minimizing the energy consumption in data center. Our algorithm considers the subsequent time intervals of data center based on profiling of VMs and PMs. An algorithm has been proposed and developed for finding near optimal solution for VMs placement with the objective of minimizing data center energy consumption. Our algorithm has been compared with a bin packing algorithm, First-Fit Decreasing (FFD), and experimental results have shown that our algorithm can reduce more energy consumption than the FFD algorithm and is scalable for larger test problems.