Energy conservation in heterogeneous server clusters

Heath, Taliver; Diniz, Bruno da Cunha; Carrera, Enrique V.; Meira, Wagner; Bianchini, Ricardo

doi:10.1145/1065944.1065969

Cited by 221 publications

(183 citation statements)

References 15 publications

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“…Most of the cluster energy management literature addresses the problem of distributing requests in a web server cluster in such a way that the performance goals are met and the energy consumption is minimized [4,5,10,19,21]. There are a number of papers that describe server or cluster level energy management using independent [8,18] or cooperative DVS techniques [7,11].…”

Section: Related Work and Conclusionmentioning

confidence: 99%

pMapper: Power and Migration Cost Aware Application Placement in Virtualized Systems

Verma¹,

Ahuja²,

Neogi³

2008

Lecture Notes in Computer Science

527

437

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Abstract. Workload placement on servers has been traditionally driven by mainly performance objectives. In this work, we investigate the design, implementation, and evaluation of a power-aware application placement controller in the context of an environment with heterogeneous virtualized server clusters. The placement component of the application management middleware takes into account the power and migration costs in addition to the performance benefit while placing the application containers on the physical servers. The contribution of this work is two-fold: first, we present multiple ways to capture the cost-aware application placement problem that may be applied to various settings. For each formulation, we provide details on the kind of information required to solve the problems, the model assumptions, and the practicality of the assumptions on real servers. In the second part of our study, we present the pMapper architecture and placement algorithms to solve one practical formulation of the problem: minimizing power subject to a fixed performance requirement. We present comprehensive theoretical and experimental evidence to establish the efficacy of pMapper.

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Section: Related Work and Conclusionmentioning

confidence: 99%

pMapper: Power and Migration Cost Aware Application Placement in Virtualized Systems

Verma¹,

Ahuja²,

Neogi³

2008

Lecture Notes in Computer Science

527

437

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“…There is a growing problem of energy consumption that points toward seeking increasingly sophisticated ways, both in hardware and software, to achieve greater energy efficiency from data center facilities [3,4,5,6,7,8]. Recent research has shown, through simulation, considerable savings for high-performance data centers through predictive thermal-aware job scheduling, i.e., scheduling that takes its thermal impact into consideration [7].…”

Section: Introductionmentioning

confidence: 99%

Energy Efficiency of Thermal-Aware Job Scheduling Algorithms under Various Cooling Models

Varsamopoulos

Banerjee

Gupta

2009

Communications in Computer and Information Science

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Abstract.One proposed technique to reduce energy consumption of data centers is thermal-aware job scheduling, i.e. job scheduling that relies on predictive thermal models to select among possible job schedules to minimize its energy needs. This paper investigates, using a more realistic linear cooling model, the energy savings of previously proposed thermal-aware job scheduling algorithms, which assume a less realistic model of constant cooling. The results show that the energy savings achieved are greater than the savings previously predicted. The contributions of this paper include: i) linear cooling models should be used in analysis and algorithm design, and ii) although the job scheduler must control the cooling equipment to realize most of the thermal-aware job schedule's savings, some savings can be still achieved without that control.

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“…A technique proposed by Heath [41] focuses on measuring power consumption via different data center devices using performance of power consumed in either homogeneous or heterogeneous racks executing different applications. The demand model proposed by Adachi [42] is more applicable to the data center industry because it combines the workloads and resources of data centers into resource pools, then provides those resources based on priority or predicted algorithm use.…”

Section: Related Workmentioning

confidence: 99%

Power Usage Effectiveness Metrics to Measure Efficiency and Performance of Data Centers

Uddin

Alsaqour

Shah

et al. 2014

Appl. Math. Inf. Sci.

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Abstract:Intensifying computation demand from enterprises has driven the growth of large, multifaceted data centers to manage current Internet, financial, commercial, and business applications. A data center comprises thousands of servers and other equipment that require substantial amounts of power to operate. This condition results in numerous challenges for the data center industry, such as massive energy consumption, underutilization of installed equipment, emission of greenhouse gases, and effect on global warming. This paper highlights the significance of identifying metrics to determine the performance and efficiency of a data center, which can help such a facility achieve operational cost savings through proper implementation of performance-measuring metrics. This paper discusses the implementation of Power Usage Effectiveness metrics in a tier-level data center in Pakistan. The results show that the overall performance value of the facility is 3.3, which indicates poor and inefficient operations.

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Energy conservation in heterogeneous server clusters

Cited by 221 publications

References 15 publications

pMapper: Power and Migration Cost Aware Application Placement in Virtualized Systems

pMapper: Power and Migration Cost Aware Application Placement in Virtualized Systems

Energy Efficiency of Thermal-Aware Job Scheduling Algorithms under Various Cooling Models

Power Usage Effectiveness Metrics to Measure Efficiency and Performance of Data Centers

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