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:10.1145/2366231.2337216

Cited by 63 publications

(101 citation statements)

References 18 publications

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“…We devise an online solution based on T-slot Lyapunov optimization to optimize both the electricity cost, and the peak power cost without significantly violating the carbon capping requirement of the data centers. Finally, while the existing work on data center peak power optimization rely on predictability of cloud parameters [47,50,51,52,61,75,116,121], we show that the prediction error of cloud parameters such as workload has a very harmful impact on the peak power optimization, and adopt stochastic programming to remove such an impact. The following sections give a detailed overview on the contributions.…”

Section: Application Of the Solutionsmentioning

confidence: 94%

“…UPS typically can power the data center for about 10-15 minutes. UPSes are primarily installed centrally in data centers, where their power is available to all the IT equipment [61]. As shown in Fig.…”

Section: Power Infrastructurementioning

confidence: 99%

“…Some modern data centers employ distributed UPSes for groups of chassis or for each server [61]. In the first case, the UPS will deliver power to the PDU.…”

Section: Power Infrastructurementioning

confidence: 99%

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Sustainable Cloud Computing

Domdouzis

2015

Green Information Technology

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Energy consumption of the data centers worldwide is rapidly growing fueled by everincreasing demand for Cloud computing applications ranging from social networking to e-commerce. Understandably, ensuring energy-efficiency and sustainability of Cloud data centers without compromising performance is important for both economic and environmental reasons. In order to achieve these objectives, this dissertation develops a cyber-physical multi-tier server and workload management architecture which operates at the local and the global (geo-distributed) data center level.This dissertation devises optimization frameworks for each tier to optimize energy consumption, energy cost and carbon footprint of the data centers. The proposed solutions are aware of various energy management tradeoffs that manifest due to the cyber-physical interactions in data centers, while providing provable guarantee on the solutions' computation efficiency and energy/cost efficiency. The local data center level energy management takes into account the impact of server consolidation on the cooling energy, avoids cooling-computing power tradeoff, and optimizes the total energy (computing and cooling energy) considering the data centers' technology trends (servers' power proportionality and the cooling power efficiency). The global data center level cost management explores the diversity of the data centers to minimize the utility cost while satisfying the carbon cap requirement of the Cloud and while dealing with the adversity of the prediction error on the data center parameters. Finally, the synergy of the local and the global data center energy and cost optimization is shown to help towards achieving carbon neutrality (net-zero) in a cost efficient manner.

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Section: Application Of the Solutionsmentioning

confidence: 94%

Section: Power Infrastructurementioning

confidence: 99%

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Sustainable Cloud Computing

Domdouzis

2015

Green Information Technology

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“…Govindan et al, propose to leverage existing UPSes to temporarily augment the utility supply during emergencies (i.e., peak power) [8]. Finally, [7] presents an energy buffering management policies for distributed per-server UPSes to smoothen power draw from grid. The above works show the efficiency of ESD to reduce electricity price and handle power emergencies in data centers and consequently a motivation for our work.…”

Section: Related Workmentioning

confidence: 99%

“…Further, researchers have suggested energy buffering using energy storage devices (ESD) such as batteries and ultra-capacitors [3]- [9]. Current data centers mainly use ESDs as power backup [4], [7]. These ESDs can also be used to store excess renewable energy and low-cost electricity during workload valleys, which can be used to meet high power demands [4]- [8].…”

Section: Introductionmentioning

confidence: 99%

Multi-tier energy buffering management for IDCs with heterogeneous energy storage devices

Abbasi

Pore

Banerjee

et al. 2013

20th Annual International Conference on High Performance Computing

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Abstract-Energy buffering, using energy storage devices (ESD), can help to smooth renewable and cut the electricity bill in Internet data centers. Recent works demonstrate the efficiency of this scheme which comes from matching the power demand of servers with low-cost and green power supply by leveraging the long-term variation of workload and renewable. However, given the variation in efficiencies of the ESDs, short term and long term variation in renewable energy, workload and energy pricing a multi-tier management is required to closely match the green and low-cost power availability with the demand. This paper proposes a multi-tier workload and energy buffering management technique that frames each tier as an optimization problem and solves them in an online and proactive way using Receding Horizon Control(RHC). The paper also conducts an analytical study to illustrate the benefits of multi-tier energy buffering management versus one tier energy buffering management in terms of increasing the renewable utilization. Analytical and simulation studies show that multitier energy buffering management can increase the utilization of the renewables as much as two times compared to one-tier management depending on the available renewable energy and power demand magnitude and variation.

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Power Capping in High Performance Computing Systems

Borghesi

Collina

Lombardi

et al. 2015

Lecture Notes in Computer Science

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Power consumption is a key factor in modern ICT infrastructure, especially in the expanding world of High Performance Computing, Cloud Computing and Big Data. Such consumption is bound to become an even greater issue as supercomputers are envisioned to enter the Exascale by 2020, granted that they obtain an order of magnitude energy efficiency gain. An important component in many strategies devised to decrease energy usage is "power capping", i.e., the possibility to constrain the system power consumption within certain power budget. In this paper we propose two novel approaches for power capped workload dispatching and we demonstrate them on a real-life high-performance machine: the Eurora supercomputer hosted at CINECA computing center in Bologna. Power capping is a feature not included in the commercial Portable Batch System (PBS) dispatcher currently in use on Eurora. The first method is based on a heuristic technique while the second one relies on a hybrid strategy which combines a CP and a heuristic approach. Both systems are evaluated and compared on simulated job traces.

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Managing distributed ups energy for effective power capping in data centers

Abstract: Abstract

Cited by 63 publications

References 18 publications

Sustainable Cloud Computing

Sustainable Cloud Computing

Multi-tier energy buffering management for IDCs with heterogeneous energy storage devices

Power Capping in High Performance Computing Systems

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