A Kernel Search Algorithm for Virtual Machine Consolidation Problem

TL;DR

This paper introduces a kernel search heuristic for large-scale virtual machine consolidation that efficiently finds high-quality solutions, outperforming traditional solvers in speed with minimal impact on solution quality.

Contribution

The paper presents a novel kernel search algorithm with an improved variable fixing strategy tailored for VM consolidation, enhancing solution speed and maintaining quality.

Findings

Significantly faster CPU times compared to MILP solver.

Variable fixing strategy improves efficiency with negligible solution degradation.

Effective for large-scale VM consolidation problems.

Abstract

Virtual machine consolidation describes the process of reallocation of virtual machines (VMs) on a set of target servers. It can be formulated as a mixed integer linear programming problem which is proven to be an NP-hard problem. In this paper, we propose a kernel search (KS) heuristic algorithm based on hard variable fixing to quickly obtain a high-quality solution for large-scale virtual machine consolidation problems (VMCPs). Since variable fixing strategies in existing KS works may make VMCP infeasible, our proposed KS algorithm employs a more efficient strategy to choose a set of fixed variables according to the corresponding reduced cost. Numerical results on VMCP instances demonstrate that our proposed KS algorithm significantly outperforms the state-of-the-art mixed integer linear programming solver in terms of CPU time, and our proposed strategy of variable fixing significantly improves the efficiency of the KS algorithm as well as the degradation of solution quality can be negligible.