Techniques for energy-efficient power budgeting in data centers

Zhan, Xin; Reda, Sherief

doi:10.1145/2463209.2488951

Cited by 26 publications

(8 citation statements)

References 10 publications

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“…Eight down flow CRAC units are located at two sides of the center. Cold air comes from under the floor through [20], [25] 2.13% linear-TP 2.45% exponential-LLC 2.73% previous-cubic [10] 4.29% previous-linear [10], [19] 6.11%…”

Section: Resultsmentioning

confidence: 99%

Power Budgeting Techniques for Data Centers

Zhan

Reda

2015

IEEE Trans. Comput.

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The development of cloud computing and data science result in rapid increases of number and scale of data centers. Because of cost and sustainability concerns, energy efficiency has been a major goal for data center architects. Focusing on reducing the cooling power and making full use of available computing power, power budgeting is an increasingly important requirement for data center operations. In this paper, we present a framework of power budgeting, considering both computing power and cooling power, in data centers to maximize the system normalized performance (SNP) of the entire center under a total power budget. Maximizing the SNP for a given power budget is equivalent to maximizing the energy efficiency. We propose a method to partition the total power budget among the cooling and computing infrastructure in a self-consistent way, where the cooling power is sufficient to extract the heat of the computing power. Intertwinedly, we devise an optimal computing power budgeting technique based on dynamic programming algorithm to determine the optimal power caps for the individual servers such that the available power could be efficiently translated to performance improvements. The optimal computing budgeting technique leverages a proposed online throughput predictor based on performance counter measurements to estimate the change in throughput of heterogeneous workloads as a function of allocated server power caps. We demonstrate that our proposed power budgeting method outperforms previous methods by 3-4 percent in terms of SNP using our data center simulation environment. While maintaining the improvement of SNP, our method improve fairness at best by 57 percent. We also evaluate the performance of our method in power saving scenario and dynamic power budgeting case.

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Section: Resultsmentioning

confidence: 99%

Power Budgeting Techniques for Data Centers

Zhan

Reda

2015

IEEE Trans. Comput.

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“…At a data center level, it translates into power budgeting, where the total power budget is partitioned among the cooling and computing units, and where the cooling power has to be sufficient to extract the heat of the computing power . Given the computing power budget, Zhan and Reda proposed an optimal computing budgeting technique based on knapsack‐solving algorithms to determine the power caps for the individual servers . Adding constraints (ie, temperature, power capping, or energy budget) leads to the design of complex shutdown policies where energy consumption belongs to the multiple criteria to optimize but generally not the predominant one …”

Section: Related Workmentioning

confidence: 99%

Quantifying the impact of shutdown techniques for energy‐efficient data centers

Raïs

Orgerie

Quinson

et al. 2018

Concurrency and Computation

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Summary Current large‐scale systems, like datacenters and supercomputers, are facing an increasing electricity consumption. These infrastructures are often dimensioned according to the workload peak. However, as their consumption is not power‐proportional when the workload is low, the power consumption is still high. Shutdown techniques have been developed to adapt the number of switched‐on servers to the actual workload. However, datacenter operators are reluctant to adopt such approaches because of their potential impact on reactivity and hardware failures, and their energy gain is often largely misjudged. In this article, we evaluate the potential gain of shutdown techniques by taking into account shutdown and boot up costs in time and energy. This evaluation is made on recent server architectures and future energy‐aware architectures. Our simulations exploit real traces collected on production infrastructures under various machine configurations with several shutdown policies, with and without workload prediction. We study the impact of future's knowledge for saving energy with such policies. Finally, we examine the energy benefits brought by suspend‐to‐disk and suspend‐to‐RAM techniques, and we study the impact of shutdown techniques on the energy consumption of prospective hardware with heterogeneous processors (big‐medium‐little paradigm).

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“…Recently, researches on cooperative control among data center units have been considered [3], [24], [25], [26]. For example, Ref.…”

Section: Cooperative Control Of Data Center Unitsmentioning

confidence: 99%

“…For example, Ref. [24] proposed an energyefficient power budgeting method for data centers considering both energy consumption of servers and that of air conditioners. In Ref.…”

Section: Cooperative Control Of Data Center Unitsmentioning

confidence: 99%

Tandem Equipment Arranged Architecture with Exhaust Heat Reuse System for Software-Defined Data Center Infrastructure

Taniguchi

Suganuma

Deguchi

et al. 2017

IEEE Trans. Cloud Comput.

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In this paper, we propose a novel energy-efficient architecture for software-defined data center infrastructures. In our proposed data center architecture, we include an exhaust heat reuse system that utilizes high-temperature exhaust heat from servers in conditioning humidity and air temperature of office space near the data center. To obtain high-temperature exhaust heat, equipment such as server racks and air conditioners are deployed in tandem so that the aisles are divided into three types: cold, hot, and super-hot. In this paper, to investigate the fundamental characteristics of our proposed data center architecture, we consider various types of data center models and conduct numerical simulations that use results obtained by experiments at an actual data center. Through simulation, we show that the total power consumption by a data center with our proposed architecture is 27% lower than that by data center with a conventional architecture. In addition, it is also shown that the proposed tandem equipment arrangement is suitable for obtaining high-temperature exhaust heat and decreasing the total power consumption significantly under a wider range of conditions than in the conventional equipment arrangement.

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Techniques for energy-efficient power budgeting in data centers

Cited by 26 publications

References 10 publications

Power Budgeting Techniques for Data Centers

Power Budgeting Techniques for Data Centers

Quantifying the impact of shutdown techniques for energy‐efficient data centers

Tandem Equipment Arranged Architecture with Exhaust Heat Reuse System for Software-Defined Data Center Infrastructure

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