Optimal Placement Algorithms for Virtual Machines

TL;DR

This paper introduces optimal algorithms for VM placement in cloud data centers, aiming to minimize the number of physical machines used, thereby reducing power consumption and improving resource efficiency.

Contribution

It presents novel linear and quadratic programming approaches that outperform existing methods in VM placement optimization.

Findings

Linear programming approach reduces the number of active physical machines.

Quadratic programming method offers improved solution quality over previous bounds.

Both methods efficiently solve large-scale VM placement problems.

Abstract

Cloud computing provides a computing platform for the users to meet their demands in an efficient, cost-effective way. Virtualization technologies are used in the clouds to aid the efficient usage of hardware. Virtual machines (VMs) are utilized to satisfy the user needs and are placed on physical machines (PMs) of the cloud for effective usage of hardware resources and electricity in the cloud. Optimizing the number of PMs used helps in cutting down the power consumption by a substantial amount. In this paper, we present an optimal technique to map virtual machines to physical machines (nodes) such that the number of required nodes is minimized. We provide two approaches based on linear programming and quadratic programming techniques that significantly improve over the existing theoretical bounds and efficiently solve the problem of virtual machine (VM) placement in data centers.