Virtual Machine Packing Algorithms for Lower Power Consumption

Virtual Machine(VM)-based flexible capacity management is an effective scheme to reduce total power consumption in the data centers. However, there remain the following issues, trade-off between power-saving and user experience, decision on VM packing plans within a feasible calculation time, and collision avoidance for multiple VM live migration processes. In order to resolve these issues, we propose two VM packing algorithms, a matching-based (MBA) and a greedy-type heuristic (GREEDY). MBA enables to decide an optimal plan in polynomial time, while GREEDY is an aggressive packing approach faster than MBA. We investigate the basic performance and the feasibility of proposed algorithms under both artificial and realistic simulation scenarios, respectively. The basic performance experiments show that the algorithms reduce total power consumption by between 18% and 50%, and MBA makes suitable VM packing plans within a feasible calculation time. The feasibility experiments show that the proposed algorithms are feasible to make packing plans for an actual supercomputer, and GREEDY has the advantage in power consumption, but MBA shows the better performance in user experience.