Optimizing Cloud Energy Consumption Using Static Task Scheduling Algorithms: A Comparative Study

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dc.contributor.authorKhiat, Abdelhamid
dc.date.accessioned2024-02-27T10:18:56Z
dc.date.available2024-02-27T10:18:56Z
dc.date.issued2023-12
dc.description.abstractCloud data centers, comprising a diverse set of heterogeneous resources working collaboratively to achieve high-performance computing, face the challenge of resource dynamism, where performance fluctuates over time. This dynamism poses complexities in task scheduling, warranting further research on the resilience of existing static task scheduling algorithms when deployed in dynamic cloud environments. This study adapts three well-known task scheduling algorithms to the cloud computing context and conducts a comprehensive comparison to assess their resilience to dynamic conditions. The evaluation, employing simulation techniques, analyzes total energy consumption and total response time as key metrics. The results offer detailed insights into the effectiveness of the adapted algorithms, providing valuable guidance for optimizing task scheduling in dynamic cloud data centers.
dc.identifier.doi10.1109/ICICS60529.2023.10330466
dc.identifier.isbn979-8-3503-0787-0
dc.identifier.issn2471-125X
dc.identifier.urihttps://dl.cerist.dz/handle/CERIST/1024
dc.publisherIEEE
dc.relation.ispartofseries14th International Conference on Information and Communication Systems (ICICS); 21-23 November 2023
dc.relation.pages6p.
dc.relation.placeIrbid, Jordan
dc.structureTechnologie Internet et Réseaux
dc.subjectCloud
dc.subjectTask scheduling
dc.subjectEnergy consumption
dc.subjectMakespan
dc.subjectMin-Min
dc.subjectMax-Min
dc.subjectSuffrage
dc.titleOptimizing Cloud Energy Consumption Using Static Task Scheduling Algorithms: A Comparative Study
dc.typeConference paper
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