Energy management for commercial servers

Lefurgy, Charles; Rajamani, Karthick; Rawson, Freeman L.; Felter, Wes; Kistler, Michael; Keller, Teresa

doi:10.1109/mc.2003.1250880

Cited by 395 publications

(230 citation statements)

References 16 publications

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“…Energy management in server clusters has been a popular area of research since the beginning of this decade [1,12]. Chen et al [6] combine server level CPU scaling techniques with the application provisioning problem in the same formulation.…”

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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“…In [5], the authors have tried to identify workload time series to dynamically regulate CPU power and frequency to optimize power consumption. Other works that apply voltage scaling, like [6] and [7], also manage the concepts of monitoring and estimation of the workload and the idea of having a pool of generic and interchangeable resources.…”

Section: Related Workmentioning

confidence: 99%

“…These constraints ensure that all the tasks are executed within a maximum time [4][5], that all the tasks will be executed once and just once in a processor 6, and that both the price of the data center and its room space will not exceed certain values 7. k m is a binary variable that is set to 1 if the machine m is used.…”

Section: A Static Off-line Data Center Optimizationmentioning

confidence: 99%

Leveraging Heterogeneity for Energy Minimization in Data Centers

Zapater

Ayala

Moya

2012

2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (Ccgrid 2012)

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Abstract-Energy consumption in data centers is nowadays a critical objective because of its dramatic environmental and economic impact. Over the last years, several approaches have been proposed to tackle the energy/cost optimization problem, but most of them have failed on providing an analytical model to target both the static and dynamic optimization domains for complex heterogeneous data centers. This paper proposes and solves an optimization problem for the energy-driven configuration of a heterogeneous data center. It also advances in the proposition of a new mechanism for task allocation and distribution of workload. The combination of both approaches outperforms previous published results in the field of energy minimization in heterogeneous data centers and scopes a promising area of research.

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“…The fan system power in high-end servers is as much as 80 Watts in 1U rack servers [7] and 240 Watts in 2U rack servers or more [1]. Figure 1 shows that fan power can reach up to 51% [22] of the overall server power budget. In addition to the increase in fan power, high fan speed introduces large noise levels in the systems.…”

Section: Introductionmentioning

confidence: 99%

Cool and save: Cooling aware dynamic workload scheduling in multi-socket CPU systems

Ayoub¹,

Rosing²

2010

2010 15th Asia and South Pacific Design Automation Conference (ASP-DAC)

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Abstract-Traditionally CPU workload scheduling and fan control in multi-socket systems have been designed separately leading to less efficient solutions. In this paper we present Cool and Save, a cooling aware dynamic workload management strategy that is significantly more energy efficient than state-of-the art solutions in multi-socket CPU systems because it performs workload scheduling in tandem with controlling socket fan speeds. Our experimental results indicate that applying our scheme gives average fan energy savings of 73% concurrently with reducing the maximum fan speed by 53%, thus leading to lower vibrations and noise levels.

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Energy management for commercial servers

Cited by 395 publications

References 16 publications

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

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

Leveraging Heterogeneity for Energy Minimization in Data Centers

Cool and save: Cooling aware dynamic workload scheduling in multi-socket CPU systems

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