Energy expenditure has become a significant fraction of data center operating costs. Recently, "geographical load balancing" has been suggested as an approach for taking advantage of the geographical diversity of Internet-scale distributed systems in order to reduce energy expenditures by exploiting the electricity price differences across regions. However, the fact that such designs reduce energy costs does not imply that they reduce energy usage. In fact, such designs often increase energy usage. This paper explores whether the geographical diversity of Internet-scale systems can be used to provide environmental gains in addition to reducing data center costs. Specifically, we explore whether geographical load balancing can encourage usage of "green" energy from renewable sources and reduce usage of "brown" energy from fossil fuels. We make two contributions. First, we derive three algorithms, with varying degrees of distributed computation, for achieving optimal geographical load balancing. Second, using these algorithms, we show that if dynamic pricing of electricity is done in proportion to the fraction of the total energy that is brown at each time, then geographical load balancing provides significant reductions in brown energy usage. However, the benefits depend strongly on the degree to which systems accept dynamic energy pricing and the form of pricing used.
For example, reference REF shows that if the electricity price is set based on the share of the brown energy to the total produced energy, GLB can reduce the brown energy usage.
Greening geographical load balancing
TNET
Computer Science