M-Convex VM Consolidation: Towards a Better VM Workload Consolidation

Abstract

Consolidating virtual machine workload is a unique feature of cloud computing platforms that greatly reduces the operating cost of the cloud data center. Correctly consolidating VMs’ workloads for a large scale cloud computing platform is nontrivial because a shortsighted scheme may save some cost in one aspect but becomes expensive in other aspects being neglected. In this paper, we present a framework that automates the VM consolidation process to improve the VMs and servers assignment whenever such improvement is possible. The proposed VM consolidation framework can achieve a balance among multiple administrative objectives (e.g., power cost, network cost) during the VM consolidation process. The solution method of solving the VM consolidation problem is designed based on the powerful and efficient semi quasi M-convex optimization framework. The proposed algorithm can also produce VM consolidation solutions that require minimal system reconfigurations (e.g., VM migrations, turning on/off servers). More importantly, the proposed algorithm can be implemented distributedly so that the scalability of the proposed framework is greatly improved. As a result, the proposed framework is efficient, scalable and highly practical.