Managing distributed UPS energy for effective power capping in data centers

Kontorinis, Vasileios; Zhang, Liuyi Eric; Akşanlı, Barış; Sampson, Jack; Homayoun, Houman; Pettis, Eddie; Tullsen, Dean M.; Rosing, Tajana

doi:10.1109/isca.2012.6237042

Cited by 125 publications

(79 citation statements)

References 22 publications

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“…This algorithm operated without any knowledge of the statistics of the workload or electricity cost processes, making it attractive in the presence of workload and pricing uncertainties. Moreover, distributed perserver UPSs that stores energy during low activity periods and uses this energy during power spikes can reduce costs for modern data centres [40]. However, designing the power infrastructure for a lower operating power point than the aggregated peak power of all servers requires dynamic techniques to avoid high peak power costs and, even worse, tripping circuit breakers.…”

Section: Advanced Use Of Energy Storage Devicesmentioning

confidence: 99%

Energy efficiency and renewable energy integration in data centres. Strategies and modelling review

Oró

Depoorter

Garcia

et al. 2015

Renewable and Sustainable Energy Reviews

192

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Section: Advanced Use Of Energy Storage Devicesmentioning

confidence: 99%

Energy efficiency and renewable energy integration in data centres. Strategies and modelling review

Oró

Depoorter

Garcia

et al. 2015

Renewable and Sustainable Energy Reviews

192

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“…Since large datacenters consume a lot of power, they usually undergo a tiered power pricing. The tier level depends on the overprovisioned input power to avoid high prices in-peak periods [28]. This can be seen as a power budget.…”

Section: Datacenter and Wan Modelsmentioning

confidence: 99%

“…By including these constraints in our framework, we model the impact of the network and create a more holis-tic multi-datacenter model. Additionally, we investigate the impact of two aspects of datacenter operation typically overlooked in previous work: tiered power pricing, which penalize the datacenter for exceeding certain level of power restrictions with as much as 5x higher energy costs [28], and WAN leasing costs/cost models, which leverage energy-aware routing. Both play a significant impact in datacenter job scheduling, reflected in our results.…”

Section: Introductionmentioning

confidence: 99%

Renewable Energy Prediction for Improved Utilization and Efficiency in Datacenters and Backbone Networks

Akşanlı

Venkatesh

Monga

et al. 2016

Computational Sustainability

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Datacenters are one of the important global energy consumers and carbon producers. However, their tight service level requirements prevent easy integration with highly variable renewable energy sources. Short-term green energy prediction can mitigate this variability. In this work, we first explore the existing short-term solar and wind energy prediction methods, and then leverage prediction to allocate and migrate workloads across geographically distributed datacenters to reduce brown energy consumption costs. Unlike previous works, we also study the impact of wide area networks (WAN) on datacenters, and investigate the use of green energy prediction to power WANs. Finally, we present two different studies connecting datacenters and WANs: the case where datacenter operators own and manage their WAN and the case where datacenters lease networks from WAN providers. The results show that prediction enables up to 90% green energy utilization, a 3x improvement over the existing methods. The cost minimization algorithm reduces expenses by up to 16% and increases performance by 27% when migrating workloads across datacenters. Furthermore, the savings increase up to 30% compared with no migration when servers are made energy-proportional. Finally, in the case of leasing the WAN, energy proportionality in routers can in-crease the profit of network providers by 1.6x.

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“…apply more advanced power capping to improve efficiency of the whole data center can be found in literature. An alternative method to power capping based on managing distributed UPS energy is presented in [9]. Interesting approach to combine IT workloads, power, cooling and renewable energy was studied in [18] but without use of power capping techniques.…”

Section: Minimization Of Costs and Energy Consumption In A Data Centementioning

confidence: 99%

Minimization of Costs and Energy Consumption in a Data Center by a Workload-Based Capacity Management

Costa

Oleksiak

Piątek

et al. 2015

Energy Efficient Data Centers

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OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible. This is an author-deposited version published in : http://oatao.univ-toulouse.fr/ Eprints ID : 15265The contribution was presented at E2DC 2014:http://www.dc4cities.eu/e2dc.html Abstract. In this paper we present an approach to improve power and cooling capacity management in a data center by taking into account knowledge about applications and workloads. We apply power capping techniques and proper cooling infrastructure configuration to achieve savings in energy and costs. To estimate values of a total energy consumption and costs we simulate both IT software/hardware and cooling infrastructure at once using the CoolEmAll SVD Toolkit. We also investigated the use of power capping to adjust data center operation to variable power supply and pricing. By better adjusting cooling infrastructure to specific types of workloads, we were able to find a configuration which resulted in energy, OPEX and CAPEX savings in the range of 4-25%.

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Managing distributed UPS energy for effective power capping in data centers

Abstract: Abstract

Cited by 125 publications

References 22 publications

Energy efficiency and renewable energy integration in data centres. Strategies and modelling review

Energy efficiency and renewable energy integration in data centres. Strategies and modelling review

Renewable Energy Prediction for Improved Utilization and Efficiency in Datacenters and Backbone Networks

Minimization of Costs and Energy Consumption in a Data Center by a Workload-Based Capacity Management

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