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Erschienen in:
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2022 | OriginalPaper | Buchkapitel

Improved Scientific Workflow Scheduling Algorithm with Distributed Heft Ranking and TBW Scheduling Method

verfasst von : Ramandeep Sandhu, Kamlesh Lakhwani

Erschienen in: IoT and Analytics for Sensor Networks

Verlag: Springer Singapore

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Abstract

Scheduling is a process that manages the workflow tasks during execution on different resources. Virtual infrastructure is a dynamic mapping of system resources to applications in order to maximize its utilization. In today's technological world, cloud has taken a long stride on the success towards maximum throughput as well as highest qualitative services to its consumers. Yet, approaches for maximizing the utilization of cloud resources are at peak demand. Each cloud service provider focuses on maximum utilization with minimum consumption of cloud resources, although managing and providing computational resources to maximum number of users and to execute such huge applications is a challenging one. In this paper, a scheduling algorithm with name TBW (Tabu Bayesian Whale Optimization) has been proposed. Basically, the algorithm is used to target the improvement in scheduling of scientific workflows. The complete framework has firstly used a ranking algorithm named distributed HEFT ranking and then applied TBW algorithm on ranked tasks of input workflows. The work has been executed for five scientific workflows LIGO, MONTAGE, Epigenomics, SIPHT and Cybershake. TBW is using tabu method on workflow tasks for fast local search in cloud system, and Bayesian Optimization is used to find out best possible combinations of resources where tasks are mapped and then whale optimization maps the tasks on the resources in a smart way. In the whole process, total execution time and cost parameters are minimized under deadline constraints.

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Metadaten
Titel
Improved Scientific Workflow Scheduling Algorithm with Distributed Heft Ranking and TBW Scheduling Method
verfasst von
Ramandeep Sandhu
Kamlesh Lakhwani
Copyright-Jahr
2022
Verlag
Springer Singapore
Buch
IoT and Analytics for Sensor Networks

Print ISBN: 978-981-16-2918-1

Electronic ISBN: 978-981-16-2919-8

Copyright-Jahr: 2022

DOI
https://doi.org/10.1007/978-981-16-2919-8_23

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