Issue No. 03 - March (2015 vol. 26)
Kejiang Ye , College of Computer Science, Zhejiang University, Hangzhou 310027, China
Zhaohui Wu , College of Computer Science, Zhejiang University, Hangzhou 310027, China
Chen Wang , CSIRO Computational Informatics , PO Box 76, Epping, NSW 1710, Australia
Bing Bing Zhou , Centre for Distributed and High Performance Computing, School of Information Technologies, University of Sydney, NSW 2006, Australia
Weisheng Si , School of Computing, Engineering, and Mathematics, University of Western Sydney, Penrith, NSW 2751, Australia
Xiaohong Jiang , College of Computer Science, Zhejiang University, Hangzhou 310027, China
Albert Y. Zomaya , Centre for Distributed and High Performance Computing, School of Information Technologies, University of Sydney, NSW 2006, Australia
Improving energy efficiency of data centers has become increasingly important nowadays due to the significant amounts of power needed to operate these centers. An important method for achieving energy efficiency is server consolidation supported by virtualization. However, server consolidation may incur significant degradation to workload performance due to virtual machine (VM) co-location and migration. How to reduce such performance degradation becomes a critical issue to address. In this paper, we propose a profiling-based server consolidation framework which minimizes the number of physical machines (PMs) used in data centers while maintaining satisfactory performance of various workloads. Inside this framework, we first profile the performance losses of various workloads under two situations: running in co-location and experiencing migrations. We then design two modules: (1) consolidation planning module which, given a set of workloads, minimizes the number of PMs by an integer programming model, and (2) migration planning module which, given a source VM placement scenario and a target VM placement scenario, minimizes the number of VM migrations by a polynomial time algorithm. Also, based on the workload performance profiles, both modules can guarantee the performance losses of various workloads below configurable thresholds. Our experiments for workload profiling are conducted with real data center workloads and our experiments on our two modules validate the integer programming model and the polynomial time algorithm.
Planning, Web servers, Virtual machining, Resource management, Databases, File servers
K. Ye et al., "Profiling-Based Workload Consolidation and Migration in Virtualized Data Centers," in IEEE Transactions on Parallel & Distributed Systems, vol. 26, no. 3, pp. 878-890, 2015.