A Cost-Effective Distributed File Service with QoS Guarantees

SIGMETRICS Perform. Eval. Rev.

et al. 2010

Self Cite

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.

“…There have been a few previous works on this topic [19,18,32,35]. Rao [32] and Shah [35] did not consider network latencies, realistic SLAs, or time-varying workloads.…”

Section: Optimization-based Request Distribution In Internet Servicesmentioning

confidence: 99%

Section: Optimization-based Request Distribution In Internet Servicesmentioning

confidence: 99%

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

Bilgir

SIGMETRICS Perform. Eval. Rev.

et al. 2010

Self Cite

“…Optimization-based request distribution in Internet services. There have been a few previous works on this topic [20], [19], [33], [36]. Rao [33] and Shah [36] did not consider network latencies, realistic SLAs, or time-varying workloads.…”

Section: Related Workmentioning

confidence: 99%

“…Moreover, these studies did not simulate or implement their proposed request distribution schemes or any competing heuristics. In contrast, [20], [19] included extensive simulations of their distribution schemes. However, none of these four works considered brown energy caps, market interactions, request types, or power mixes.…”

Section: Related Workmentioning

confidence: 99%

Capping the brown energy consumption of Internet services at low cost

International Conference on Green Computing

Nguyen

et al. 2010

Self Cite

155

139

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 a policy for request distribution across the data centers. Our policy can be used to abide by caps on brown energy consumption, such as those that might arise from Kyoto-style carbon limits, from corporate pledges on carbon-neutrality, or from limits imposed on services to encourage brown energy conservation. We evaluate our framework and policy extensively through simulations and real experiments. Our results show how our policy allows a service to trade off consumption and cost. For example, using our policy, the service can reduce brown energy consumption by 24% for only a 10% increase in cost, while still abiding by SLAs.

“…There have been a few works on cost-aware job placement in wide-area, grid scenarios [6,17]. In the context of a wide-area distributed file service, Le et al [28] considered cost-aware request distribution. Unfortunately, these works did not consider energy costs, peak power costs, or any cooling issues.…”

Section: Background and Related Workmentioning

confidence: 99%

Reducing electricity cost through virtual machine placement in high performance computing clouds

Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis

Zhang

et al. 2011

Self Cite

149

Cloud service providers operate multiple geographically distributed data centers. These data centers consume huge amounts of energy, which translate into high operating costs. Interestingly, the geographical distribution of the data centers provides many opportunities for cost savings. For example, the electricity prices and outside temperatures may differ widely across the data centers. This diversity suggests that intelligently placing load may lead to large cost savings. However, aggressively directing load to the cheapest data center may render its cooling infrastructure unable to adjust in time to prevent server overheating. In this paper, we study the impact of load placement policies on cooling and maximum data center temperatures. Based on this study, we propose dynamic load distribution policies that consider all electricity-related costs as well as transient cooling effects. Our evaluation studies the ability of different cooling strategies to handle load spikes, compares the behaviors of our dynamic cost-aware policies to cost-unaware and static policies, and explores the effects of many parameter settings. Among other interesting results, we demonstrate 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.