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Cluster Computing
Erschienen in: Cluster Computing 2/2021

23.07.2020

Energy and resource efficient workflow scheduling in a virtualized cloud environment

verfasst von: Neha Garg, Damanpreet Singh, Major Singh Goraya

Erschienen in: Cluster Computing | Ausgabe 2/2021

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Abstract

High energy consumption (EC) is one of the leading and interesting issue in the cloud environment. The optimization of EC is generally related to scheduling problem. Optimum scheduling strategy is used to select the resources or tasks in such a way that system performance is not violated while minimizing EC and maximizing resource utilization (RU). This paper presents a task scheduling model for scheduling the tasks on virtual machines (VMs). The objective of the proposed model is to minimize EC, maximize RU, and minimize workflow makespan while preserving the task’s deadline and dependency constraints. An energy and resource efficient workflow scheduling algorithm (ERES) is proposed to schedule the workflow tasks to the VMs and dynamically deploy/un-deploy the VMs based on the workflow task’s requirements. An energy model is presented to compute the EC of the servers. Double threshold policy is used to perceive the server’ status i.e. overloaded/underloaded or normal. To balance the workload on the overloaded/underloaded servers, live VM migration strategy is used. To check the effectiveness of the proposed algorithm, exhaustive simulation experiments are conducted. The proposed algorithm is compared with power efficient scheduling and VM consolidation (PESVMC) algorithm on the accounts of RU, energy efficiency and task makespan. Further, the results are also verified in the real cloud environment. The results demonstrate the effectiveness of the proposed ERES algorithm.
Vorheriger Artikel Two new collaborative filtering approaches to solve the sparsity problem
Nächster Artikel Self-tuning serverless task farming using proactive elasticity control

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Metadaten
Titel
Energy and resource efficient workflow scheduling in a virtualized cloud environment
verfasst von
Neha Garg
Damanpreet Singh
Major Singh Goraya
Publikationsdatum
23.07.2020
Verlag
Springer US
Erschienen in
Cluster Computing / Ausgabe 2/2021
Print ISSN: 1386-7857
Elektronische ISSN: 1573-7543
DOI
https://doi.org/10.1007/s10586-020-03149-4

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