Leveraging a predictive model of the workload for intelligent slot allocation schemes in energy-efficient HPC clusters

Cocaña-Fernández, Alberto; Sánchez, Luciano; Ranilla, José

doi:10.1016/j.engappai.2015.10.003

Cited by 10 publications

(12 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Given the complexity of finding the optimal amount of slots at every moment in a cluster architecture prone to sharp changes in the workload patterns, both reactive and proactive strategies are introduced in our previous works [45,46]. In this section, we include a brief summary of these strategies.…”

Section: Optimising Slot Allocationmentioning

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

Self Cite

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: Optimising Slot Allocationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

2019

Self Cite

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 approach allows the system to respond to smaller changes in the cluster load more frequently, thus progressively adapting its resources to better match workload valleys produced when jobs release their resources upon completion. Further information on the Hybrid Genetic Fuzzy System can be found in references [38,39].…”

Section: Decision-making Mechanismmentioning

confidence: 99%

“…Firstly, as shown in [38,39], closed sets of expert-defined rules are not optimal to every scenario, leading to a lack of flexibility when it comes to complying with the preferences and tolerances of real-world cluster administrators in terms of impact in service quality and node reliability. Secondly, these solutions require its expert system to be tuned by hand, what is a complex task and is likely to conduce to incorrectly-configured systems that can cause substantial interferences with the cluster operation, such as node thrashing or reduction of its productivity, as demonstrated in [40].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

2016

Self Cite

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

Energy-saving scheduling on IaaS HPC cloud environments based on a multi-objective genetic algorithm

et al. 2018

View full text Add to dashboard Cite

Leveraging a predictive model of the workload for intelligent slot allocation schemes in energy-efficient HPC clusters

Cited by 10 publications

References 32 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

Energy-saving scheduling on IaaS HPC cloud environments based on a multi-objective genetic algorithm

Contact Info

Product

Resources

About