Cherub: Power Consumption Aware Cluster Resource Management

Kiertscher, Simon; Zinke, Jörg; Gasterstadt, Stefan; Schnor, Bettina

doi:10.1109/greencom-cpscom.2010.83

Cited by 6 publications

(7 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This work, however, focuses on adaptive resource clusters, a method that consists of automatically reshaping the cluster resources to fit the current demand by powering on or off its compute nodes, thus saving energy whenever these are underused. This method has already been applied to load-balancing clusters [13][14][15][16][17][18], in virtual data centres running VMware vSphere (VMware Distributed Power Management Concepts and Use, http://www.vmware.com/files/pdf/Distributed-Power-Management-vSphere.pdf and Citrix XenServer (Citrix XenServer-Efficient Server Virtualization Software, http://www.citrix.com/products/xenserver/overview.html hypervisors, and also in HPC clusters [19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

“…First, the optimal amount of logical resources for the cluster must be determined to balance service quality and energy savings. This can be done following either the proposals of other authors [19][20][21] or our previously proposed reactive [45] and proactive strategies [46]. Given the better results achieved with our proposed strategies in terms of flexibility, service quality compliance, and direct energy savings, we use these in this paper.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Eco-Efficient Resource Management in HPC Clusters through Computer Intelligence Techniques

2019

View full text Add to dashboard Cite

High Performance Computing Clusters (HPCCs) are common platforms for solving both up-to-date challenges and high-dimensional problems faced by IT service providers. Nonetheless, the use of HPCCs carries a substantial and growing economic and environmental impact, owing to the large amount of energy they need to operate. In this paper, a two-stage holistic optimisation mechanism is proposed to manage HPCCs in an eco-efficiently manner. The first stage logically optimises the resources of the HPCC through reactive and proactive strategies, while the second stage optimises hardware allocation by leveraging a genetic fuzzy system tailored to the underlying equipment. The model finds optimal trade-offs among quality of service, direct/indirect operating costs, and environmental impact, through multiobjective evolutionary algorithms meeting the preferences of the administrator. Experimentation was done using both actual workloads from the Scientific Modelling Cluster of the University of Oviedo and synthetically-generated workloads, showing statistical evidence supporting the adoption of the new mechanism.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Eco-Efficient Resource Management in HPC Clusters through Computer Intelligence Techniques

2019

View full text Add to dashboard Cite

show abstract

“…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

View full text Add to dashboard Cite

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.

show abstract

“…In the study of cluster's power and performance management, some works [4][5][6] test their management strategies in real environment, but the cluster scale is small, so the effectiveness of their strategies is only verified in small-scale clusters. Some works [1,[7][8][9][10][11][12] use simulation tests to evaluate their strategies.…”

Section: Related Workmentioning

confidence: 99%

A Power and Performance Management Simulation Platform for Web Application Server Cluster

Xiong

Xue

Cai

et al. 2016

IJFGCN

View full text Add to dashboard Cite

show abstract

Cherub: Power Consumption Aware Cluster Resource Management

Cited by 6 publications

References 4 publications

Eco-Efficient Resource Management in HPC Clusters through Computer Intelligence Techniques

Eco-Efficient Resource Management in HPC Clusters through Computer Intelligence Techniques

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

A Power and Performance Management Simulation Platform for Web Application Server Cluster

Contact Info

Product

Resources

About