Power Efficient Virtual Machine Packing for Green Datacenter

Abstract

Cloud computing is now considered to be a new computing paradigm to provide scalable Infrastructure, Platform and Software as a Service via the Internet. While, the diffusion of Cloud computing is expected to cause an explosive increase in power consumption for IT resources in data centers. 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 employ two power consumption models, one is the linear model and the other is piecewise linear model. In the linear model, the feasibility experiments show that the reduction ratio of total power consumption observed with MBA is smaller than that of GREEDY, but the performance degradation of MBA is less than that of GREEDY. In the piecewise-linear model, the feasibility experiments show that MBA investigates more reducing power consumption than GREEDY. The performance degradation of MBA is also less than GREEDY.