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

• Published in: IEEE transactions on cloud computing Vol. 4; no. 4; pp. 415 - 428
• Main Authors: Zhe Huang, Tsang, Danny H. K.
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE Computer Society 01-10-2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithm design and analysis; Cloud computing; Consolidation; Cost reduction; discrete optimization; Linear programming; Load management; M-convex; Optimization; Servers; service level agreement; virtual machine consolidation; Virtual machining; Virtual networks; Workload
• ISSN: 2168-7161; 2168-7161; 2372-0018
• DOI: 10.1109/TCC.2014.2369423

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.