Cost-Aware Virtual Machine Placement in Cloud Computing Systems

Cloud service providers operate multiple geographically distributed data centers. These data centers consume huge amounts of energy, which translate into high operating costs. Interestingly, the geographical distribution of the data centers provides many opportunities for cost savings. For example, the electricity prices and outside temperatures may be widely different at the data centers. This diversity suggests that intelligently placing load across the data centers may lead to large cost savings. However, aggressively directing load to the cheapest data center may render its cooling infrastructure unable to adjust in time to prevent server overheating. In this paper, we study the impact of load placement policies on cooling and maximum data center temperatures. Based on this study, we propose load placement policies that consider, not only all electricity-related costs, but also transient cooling effects. Our evaluation studies the ability of different cooling strategies to handle load spikes, demonstrates the behavior of our policies as compared to their simpler counterparts, and explores the effect of many parameter settings. Among other interesting results, we demonstrate that (1) our policies can provide large cost savings, (2) load migration enables savings in many scenarios, and (3) all electricity-related costs must be considered at the same time for higher and consistent cost savings.