Cost- and Energy-Aware Load Distribution Across Data Centers

Today, many large organizations operate multiple data centers. The reasons for this include natural business distribution, the need for high availability and disaster tolerance, the sheer size of their computational infrastructure, and/or the desire to provide uniform access times to the infrastructure from widely distributed client sites. Regardless of the reason, these organizations consume significant amounts of energy and this energy consumption has both a financial and environmental cost. Interestingly, the geographical distribution of the data centers often exposes many opportunities for optimizing energy consumption and costs by intelligently distributing the computational workload. We are interested in three such opportunities. First, we seek to exploit data centers that pay different and perhaps variable electricity prices. In fact, many power utilities now allow consumers to choose hourly pricing, e.g. [1]. Second, we seek to exploit data centers that are located in different time zones, which adds an extra component to price variability. For example, one data center may be under peakdemand prices while others are under off-peak-demand prices. Third, we seek to exploit data centers located near sites that produce renewable (hereafter called “green”) electricity to reduce “brown” energy consumption that is mostly produced by carbon-intensive means, such as coal-fired power plants. To make our investigation of these degrees of freedom more concrete, in this paper we consider multi-datacenter Internet services, such as Google or iTunes. These services place their data centers behind a set of front-end devices. The front-ends are responsible for inspecting each client request and forwarding it to one of the data centers that can serve it, according to a request distribution policy. Despite their wide-area distribution of requests, services must strive not to violate their servicelevel agreements (SLAs). This paper proposes and evaluates a framework for optimization-based request distribution. The framework enables services to manage their energy consumption and costs, while respecting their SLAs. It also allows services to take full advantage of the degrees of freedom mentioned above. Based on the framework, we propose two request distribution policies. For comparison, we also propose a greedy heuristic designed with the same goals and constraints as the other policies. Operationally, an optimization-based policy defines the fraction of the clients’ requests that should be directed to each data center. The front-ends periodically (e.g., once per hour) solve the optimization problem defined by the policy. After fractions are computed, the front-ends abide by them until they are recomputed. The heuristic policy operates quite differently. During each hour, it first exploits the data centers with the best power efficiency, and then starts exploiting the data centers with the cheapest electricity. Our evaluation uses a day-long trace from a commercial service. Our results show that the optimizationbased policies can accrue substantial cost reductions by intelligently leveraging time zones and hourly electricity prices. The results also show that we can exploit green energy to achieve significant reductions in brown energy consumption for small increases in cost.