Leveraging a predictive model of the workload for intelligent slot allocation schemes in energy-efficient HPC clusters
| Autor: | Luciano Sánchez, José Ranilla, Alberto Cocaña-Fernández |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
020203 distributed computing
Optimization problem Computer science business.industry Quality of service Distributed computing Supervised learning Evolutionary algorithm Workload 02 engineering and technology Machine learning computer.software_genre Resource (project management) Artificial Intelligence Control and Systems Engineering 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence Electrical and Electronic Engineering Predictability business computer Efficient energy use |
| Zdroj: | Engineering Applications of Artificial Intelligence. 48:95-105 |
| ISSN: | 0952-1976 |
| DOI: | 10.1016/j.engappai.2015.10.003 |
| Popis: | A proactive mechanism to learn an efficient strategy for adaptive resource clusters is proposed. In contrast to reactive techniques, that rescale the cluster to fit the past load, a predictive strategy is adopted. The cluster incoming workload is forecasted and an optimization problem is defined whose solution is the optimal action according to a utility function. Genetic-based machine learning techniques are used, including multi-objective evolutionary algorithms under the distal supervised learning setup. Experimental evaluations show that the proactive system presented in this work improves either the energetic efficiency or the number of reconfigurations of previous approaches without a loss in the quality of service. Depending on the predictability of the workload, in real world cluster scenarios additional energy savings of up to approximately 40% were measured over the best previous approach, with a 2× factor increment in the number of reconfigurations. |
| Databáze: | OpenAIRE |
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