Greening geographical load balancing
Energy expenditure has become a significant fraction of data center operating costs. Recently, “geographical load balancing” has been suggested to reduce energy cost by exploiting the electricity price differences across regions. However, this reduction of cost can paradoxically increase total energy use. This work explores whether the geographical diversity of internet-scale systems can additionally be used to provide environmental gains. We first focus on geographical load balancing, which is modeled as a convex optimization problem. We derive two distributed algorithms for achieving optimal geographical load balancing and characterize the optimal solutions. Then we continue to use the framework and algorithms to investigate whether geographical load balancing can encourage use of “green” renewable energy and reduce use of “brown” fossil fuel energy. Here we consider two approaches, namely, dynamic pricing and local renewables. For the dynamic pricing case, our numeric results show that if electricity is dynamically priced in proportion to the instantaneous fraction of the total energy that is brown, then geographical load balancing provides significant reductions in brown energy use. However, the benefits depend strongly on the degree to which systems accept dynamic energy pricing and the form of pricing used. For the local renewables case, we perform a trace-based study to evaluate three issues related to achieving this goal: the impact of geographical load balancing, the role of storage, and the optimal mix of renewables. Our results highlight that geographical load balancing can significantly reduce the required capacity of renewable energy by using the energy more efficiently with “follow the renewables” routing. Further, our results show that small-scale storage can be useful, especially in combination with geographical load balancing, and that an optimal mix of renewables includes significantly more wind than photovoltaic solar.
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