A software tool to efficiently manage the energy consumption of HPC clusters

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

doi:10.1109/fuzz-ieee.2015.7338079

Cited by 7 publications

(9 citation statements)

References 26 publications

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“…retrieved by the daemon, as well as manually reconfiguring compute nodes and choosing the parameters that rule the decision-making mechanism. A detailed description of the architecture of EECluster and its modules can be found in Reference [22].…”

Section: Architecturementioning

confidence: 99%

“…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%

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

2019

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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.

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Section: Architecturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

2019

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“…For example, different scheduling techniques which switch off the idle nodes to save their energy consumption, were presented in [35,12,25] and [11]. In [9] and [4], a heuristic to manage the workloads between the computing resources of the cluster and reduce their energy consumption, was published. However, the dynamic voltage and frequency scaling (DVFS) is the most popular technique to reduce the energy consumption of computing processors.…”

Section: Related Workmentioning

confidence: 99%

Energy consumption reduction for asynchronous message-passing applications

et al. 2016

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It is widely accepted that the asynchronous parallel methods are more suitable than the synchronous ones on a grid architecture. Indeed, they outperform the synchronous methods because they overlap the communications of the synchronous methods with computations. However, they also usually execute more iterations than the synchronous ones and thus consume more energy. To reduce the energy consumption of the CPUs executing such methods, the Dynamic voltage and frequency scaling (DVFS) technique can be used. It lowers the frequency of a CPU to reduce its energy consumption, but it also decreases its computing power. Therefore, the frequency that gives the best trade-off between energy consumption and performance must be selected. This paper presents a new online frequency selecting algorithm for parallel iterative asynchronous methods running over grids. It selects a vector of frequencies that gives the best trade-off between energy consumption and performance. New energy and performance models were used in this algorithm to predict the execution time and the energy consumption of synchronous, asynchronous or hybrid iterative applications running over grids. The proposed algorithm was evaluated on the SimGrid simulator. The experiments showed that synchronously applying the proposed algorithm to the asynchronous version of the application reduces on average its energy consumption by 22% and speeds it up by 5.72%. Finally, the proposed algorithm was also compared to a method that uses the well known energy and delay product and the comparison results showed that it outperforms this method in terms of energy consumption and performance.

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“…This fundamentally limits their applicability to real world clusters which require sufficient flexibility to find an optimal balance between service quality, energy savings and node thrashing within the cluster administrator tolerances. Consequently, we introduced in [10] the software tool EECluster to improve the efficiency of HPCCs while complying with administrator preferences. EECluster achieves this due to the Hybrid Genetic Fuzzy System (HGFS) that uses as decision-making mechanism, combining expert-defined knowledge with computergenerated rules elicited from past workload records from the cluster.…”

Section: Introductionmentioning

confidence: 99%

Improving EECluster to optimize the carbon footprint and operating costs of HPC clusters

Cocaña-Fernández

Ranilla

Sánchez

2019

2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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High Performance Computing Clusters (HPCCs) are essential platforms for solving up-to-date challenges through parallel and distributed applications. Nevertheless, HPCCs have an important economic and environmental impact owing to the large amounts of energy required for their operation. In this work, an improved version of our EECluster software focused on reducing the operating costs and emissions of HPCCs is presented. Multicriteria learning algoritms are leveraged to jointly optimize cluster performance, direct and indirect operating costs both from an economic and an environmental standpoint. EECluster is a mature software that can be integrated with the resource management systems OGE/SGE and PBS/TORQUE.

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A software tool to efficiently manage the energy consumption of HPC clusters

Cited by 7 publications

References 26 publications

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

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

Energy consumption reduction for asynchronous message-passing applications

Improving EECluster to optimize the carbon footprint and operating costs of HPC clusters

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