An Energy Manager for High Performance Computer Clusters

Alvarruiz, Fernando; Alfonso, Carlos; Caballer, Miguel; Hern'ndez, Vicente

doi:10.1109/ispa.2012.38

Cited by 28 publications

(54 citation statements)

References 10 publications

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“…In another work Freeh et al [14] presents a work which uses techniques to reduce the processor's frequency in a cluster to reduce power consumption. F. Alvarruiz et al [15] proposed a work called CLUES which replaces idle state with power off state. In idle state energy savings of 3.42% were achieved, and 66.67% of energy was saved in power off state, it showed a energy saving of 66.67%.…”

Section: Related Workmentioning

confidence: 99%

Power Optimization of CFD Applications on Heterogeneous Architectures

Ahmed¹,

Khan²,

Ahmed³

et al. 2017

IJCA

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Computational fluid dynamics (CFD) is widely used by the scientific computing community to solve various fluid flow problems. High performance computing (HPC) enable faster CFD simulations with higher solution accuracies. Many CFD applications are run on multicore and multiprocessor platforms and are being increasingly ported to run on computational accelerators such as graphical processing units (GPUs) to increase the performance. The increase of computational power is necessary to allow faster computing, which in turn increases the power consumption. The issue of the power consumption has to be addressed, particularly in applications such as CFD, where the simulations may need to be executed iteratively for a large number of times, each iteration taking a large amount of time, in order to obtain appropriately accurate results. Poweraware computing is concerned with devising energy efficient methods. Techniques such as Dynamic voltage and frequency scaling (DVFS) and offlining can be used to reduce power consumption and increase the energy efficiency of applications. In this paper, we present a combination of these techniques and apply to CFD applications running on heterogeneous architectures. We reduce the overall power consumption with a small performance loss. Precisely, for DVFS, we save 4% to 23.5% of energy with a performance loss of 0.6% to 9.8%. Similarly, for online-offline mode, we save 22% of energy with a performance loss of 0.3%.

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Section: Related Workmentioning

confidence: 99%

Power Optimization of CFD Applications on Heterogeneous Architectures

Ahmed¹,

Khan²,

Ahmed³

et al. 2017

IJCA

View full text Add to dashboard Cite

show abstract

“…Whenever idle resources are detected, the clusters dynamically and automatically scale in in order to cut down the costs in the case of using a public Cloud provider. The elasticity management is carried out by the front-end node of the cluster, with the help of CLUES [32]. More details about the elasticity management are available in [3].…”

Section: Elastic Cloud Computing Cluster (Ec3)mentioning

confidence: 99%

Self-managed cost-efficient virtual elastic clusters on hybrid Cloud infrastructures

Calatrava

Romero

Moltó

et al. 2016

Future Generation Computer Systems

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This paper describes the developments to produce EC3 (Elastic Cloud Computing Cluster), a tool that creates self-managed cost-efficient virtual hybrid elastic clusters on top of Infrastructure as a Service (IaaS) Clouds. Using spot instances, together with checkpointing techniques, EC3 can significantly reduce the total cost of executions while introducing automatic fault tolerance. Moreover, EC3 can deploy and manage hybrid clusters across on-premises and public Cloud resources, thus introducing Cloud bursting capabilities.A case study is presented to assess the effectiveness of the tool featuring the structural dynamic analysis of buildings. In addition, checkpointing algorithms are evaluated in a real Cloud environment with existing workloads to study their effectiveness. The results show the feasibility and benefits of this type of clusters for computationally intensive applications.

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“…As mentioned in the introduction, the dependence between the energy consumption and the Quality of Service has been studied, and different strategies were proposed to improve their balance [35][36][37][38][39][40]. In this work, these studies are updated by including other sources of carbon dioxide emissions that are originated in the life cycle of a compute node.…”

Section: Eco-efficiencymentioning

confidence: 99%

“…This technique has been applied to Load-Balancing clusters in [27][28][29][30][31][32] and in VMware vSphere [33] and Citrix XenServer [34] hypervisors. Recently, various software tools implementing this technique in HPC clusters have also been developed [35][36][37].…”

Section: Introductionmentioning

confidence: 99%

Improving the Eco-Efficiency of High Performance Computing Clusters Using EECluster

2016

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As data and supercomputing centres increase their performance to improve service quality and target more ambitious challenges every day, their carbon footprint also continues to grow, and has already reached the magnitude of the aviation industry. Also, high power consumptions are building up to a remarkable bottleneck for the expansion of these infrastructures in economic terms due to the unavailability of sufficient energy sources. A substantial part of the problem is caused by current energy consumptions of High Performance Computing (HPC) clusters. To alleviate this situation, we present in this work EECluster, a tool that integrates with multiple open-source Resource Management Systems to significantly reduce the carbon footprint of clusters by improving their energy efficiency. EECluster implements a dynamic power management mechanism based on Computational Intelligence techniques by learning a set of rules through multi-criteria evolutionary algorithms. This approach enables cluster operators to find the optimal balance between a reduction in the cluster energy consumptions, service quality, and number of reconfigurations. Experimental studies using both synthetic and actual workloads from a real world cluster support the adoption of this tool to reduce the carbon footprint of HPC clusters.

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An Energy Manager for High Performance Computer Clusters

Cited by 28 publications

References 10 publications

Power Optimization of CFD Applications on Heterogeneous Architectures

Power Optimization of CFD Applications on Heterogeneous Architectures

Self-managed cost-efficient virtual elastic clusters on hybrid Cloud infrastructures

Improving the Eco-Efficiency of High Performance Computing Clusters Using EECluster

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