Reducing electricity cost through virtual machine placement in high performance computing clouds

Abstract

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 differ widely across the data centers. This diversity suggests that intelligently placing load 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 dynamic load distribution policies that consider all electricity-related costs as well as transient cooling effects. Our evaluation studies the ability of different cooling strategies to handle load spikes, compares the behaviors of our dynamic cost-aware policies to cost-unaware and static policies, and explores the effects 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.

Exploiting CPU-Load and Data Correlations in Multi-Objective VM Placement for Geo-Distributed Data Centers

“…In addition, an energyefficient management is missing from these works based on existing CPU-load correlation to achieve more energy and cost savings. Authors in [14] presented a workload assignment and migration technique to minimize the costs of energy consumed by IT and cooling equipment considering the fluctuations of electricity price and the variability of the DCs' Power Usage Effectiveness (PUE). Zhao et al [15] addressed the problem of dynamic pricing by designing an efficient online job scheduling and server provisioning in each DC to maximize the timeaverage overall profit of the cloud provider with respect to delay constraints.…”

Profit Aware Load Balancing for Distributed Cloud Data Centers

“…These studies help to replace the usage of "brown" energy (produced via carbon-intensive means) with "green energy" during a data center's operation in order to cut down the cost spent on energy consumptions. For instance, a study of a framework for multidata-center services was introduced in [10] [11]. However, no SLA-based profit was considered in these studies.…”

Delay-Sensitive Service Request Scheduling for Cloud Computing

“…These studies help replace the usage of "brown" energy (produced via carbon-intensive means) with "green energy" during a data center's operation in order to cut down the cost spent on energy consumptions. Frameworks for multi data center service was introduced in [65][66].…”