Managing energy usage and cost through load distribution in multi-data-center services

Multi-data-center services will soon be common place. These services raise many questions about how exactly to distribute the offered load across the data centers. In fact, different load distributions may produce wildly different monetary costs, performance, and/or energy consumption. Moreover, these large services consume large amounts of energy produced via carbon-dioxide-intensive means. We refer to this as “brown energy”, in contrast to renewable or “green” energy. This large energy consumption represents significant and fast-growing financial and environmental costs. Increasingly, services are exploring dynamic methods to manage energy and energy-related costs while respecting their service-level agreements (SLAs). Furthermore, it will soon be important for these services to manage their usage of brown or green energy. Based on these observations, this dissertation explores load distribution policies that minimize cost or energy consumption, while respecting the services' performance requirements. First, we design, implement, and evaluate software support for multi-data-center Internet service providers to take advantage of temporal and spatial variability of electricity prices and on-site generation of green energy. Second, we extend this framework with support for capping brown energy consumption without excessively increasing costs or degrading the performance of Internet services. Third, we design, implement, and evaluate cost-aware policies for placing and migrating virtual machines in high performance cloud computing services.Our results show that our framework is very effective at allowing services to trade off brown energy consumption and cost. For example, using our policies, the service can reduce brown energy consumption by 24% for only a 10% increase in cost, while still abiding by SLAs. Our virtual machine migration framework demonstrates 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. Overall, our work demonstrates the value of load distribution across data centers, formal optimization techniques, workload prediction, and electricity price prediction for energy management.