Managing the cost, energy consumption, and carbon footprint of internet services

Abstract

The large amount of energy consumed by Internet services represents significant and fast-growing financial and environmental costs. Increasingly, services are exploring dynamic methods to minimize energy costs while respecting their service-level agreements (SLAs). Furthermore, it will soon be important for these services to manage their usage of "brown energy" (produced via carbon-intensive means) relative to renewable or "green" energy. This paper introduces a general, optimization-based framework for enabling multi-data-center services to manage their brown energy consumption and leverage green energy, while respecting their SLAs and minimizing energy costs. Based on the framework, we propose policies for request distribution across the data centers. Our policies can be used to abide by caps on brown energy consumption, such as those that might arise from Kyotostyle carbon limits, from corporate pledges on carbon-neutrality, or from limits imposed on services to encourage brown energy conservation. We evaluate our framework and policies extensively through simulations and real experiments. Our results show how our policies allow a service to trade off 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.

Minimizing the Operational Cost of Data Centers via Geographical Electricity Price Diversity

“…To achieve energy efficiency for such cloud service providers, a promising approach is to explore the time-varying electricity prices in these regions where the data centers are located at. However, minimizing the energy consumption of geographically distributed data centers is essentially different from that of a single data center, this poses a new challenge to design efficient algorithms for energy management in such geographically distributed data centers, and several efforts have been taken in the past several years [15], [2], [16], [17], [6], [12], [20], [13], [14]. For example, Qureshi et al [15] initialized the study by characterizing the energy expense per unit of computation due to fluctuating electricity prices.…”

“…Guo et al [6] made use of the Lyapunov optimization technique to reduce the electricity bill of data centers, using temporary energy storages like UPS. Le et al [12] devised a general framework to manage the usage of "brown energy" (produced via carbon-intensive means) and "green" energy (renewable energy) with the aim of reducing environmental effects on huge amount of energy consumed by the data centers. Zhang et al [20] investigated the problem of geographical request allocation to maximize the usage of renewable energy under a given operation budget.…”

Capping the brown energy consumption of Internet services at low cost

“…(Our technical report [18] includes a cap-and-pay policy as well.) Subsection III-B2 describes the instantiation of the parameters.…”

“…To model these services, all we need to do is specify "infinite" energy caps and 100%-0% power mixes for all data centers. Our report [18] includes results for these services. Complete framework and policies.…”

“…Nevertheless, if a period of instability is ever detected, our policy can be tuned to mitigate the impact of the instability on the request distribution. Our longer technical report [18] includes more detailed market data and an extended discussion of our observations.…”