Evolutionary Multi-Objective Workflow Scheduling in Cloud

Cloud computing provides promising platforms for executing large applications with enormous computational resources to offer on demand. In a Cloud model, users are charged based on their usage of resources and the required quality of service (QoS) specifications. Although there are many existing wor...

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Vydané v:	IEEE transactions on parallel and distributed systems Ročník 27; číslo 5; s. 1344 - 1357
Hlavní autori:	Zhu, Zhaomeng, Zhang, Gongxuan, Li, Miqing, Liu, Xiaohui
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.05.2016 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Cloud computing Computation Computational modeling Encoding evolutionary algorithm Infrastructure as a Service multi-objective optimization Optimization Platforms Pricing Processor scheduling Production scheduling Quality of service Schedules Scheduling Scheduling algorithms Workflow workflow scheduling Cloud computing infrastructure as a service workflow scheduling multi-objective optimization evolutionary algorithm
ISSN:	1045-9219, 1558-2183
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Abstract	Cloud computing provides promising platforms for executing large applications with enormous computational resources to offer on demand. In a Cloud model, users are charged based on their usage of resources and the required quality of service (QoS) specifications. Although there are many existing workflow scheduling algorithms in traditional distributed or heterogeneous computing environments, they have difficulties in being directly applied to the Cloud environments since Cloud differs from traditional heterogeneous environments by its service-based resource managing method and pay-per-use pricing strategies. In this paper, we highlight such difficulties, and model the workflow scheduling problem which optimizes both makespan and cost as a Multi-objective Optimization Problem (MOP) for the Cloud environments. We propose an evolutionary multi-objective optimization (EMO)-based algorithm to solve this workflow scheduling problem on an infrastructure as a service (IaaS) platform. Novel schemes for problem-specific encoding and population initialization, fitness evaluation and genetic operators are proposed in this algorithm. Extensive experiments on real world workflows and randomly generated workflows show that the schedules produced by our evolutionary algorithm present more stability on most of the workflows with the instance-based IaaS computing and pricing models. The results also show that our algorithm can achieve significantly better solutions than existing state-of-the-art QoS optimization scheduling algorithms in most cases. The conducted experiments are based on the on-demand instance types of Amazon EC2; however, the proposed algorithm are easy to be extended to the resources and pricing models of other IaaS services.
AbstractList	Cloud computing provides promising platforms for executing large applications with enormous computational resources to offer on demand. In a Cloud model, users are charged based on their usage of resources and the required quality of service (QoS) specifications. Although there are many existing workflow scheduling algorithms in traditional distributed or heterogeneous computing environments, they have difficulties in being directly applied to the Cloud environments since Cloud differs from traditional heterogeneous environments by its service-based resource managing method and pay-per-use pricing strategies. In this paper, we highlight such difficulties, and model the workflow scheduling problem which optimizes both makespan and cost as a Multi-objective Optimization Problem (MOP) for the Cloud environments. We propose an evolutionary multi-objective optimization (EMO)-based algorithm to solve this workflow scheduling problem on an infrastructure as a service (IaaS) platform. Novel schemes for problem-specific encoding and population initialization, fitness evaluation and genetic operators are proposed in this algorithm. Extensive experiments on real world workflows and randomly generated workflows show that the schedules produced by our evolutionary algorithm present more stability on most of the workflows with the instance-based IaaS computing and pricing models. The results also show that our algorithm can achieve significantly better solutions than existing state-of-the-art QoS optimization scheduling algorithms in most cases. The conducted experiments are based on the on-demand instance types of Amazon EC2; however, the proposed algorithm are easy to be extended to the resources and pricing models of other IaaS services.
Author	Gongxuan Zhang Miqing Li Zhaomeng Zhu Xiaohui Liu
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Snippet	Cloud computing provides promising platforms for executing large applications with enormous computational resources to offer on demand. In a Cloud model, users...
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SubjectTerms	Algorithms Cloud computing Computation Computational modeling Encoding evolutionary algorithm Infrastructure as a Service multi-objective optimization Optimization Platforms Pricing Processor scheduling Production scheduling Quality of service Schedules Scheduling Scheduling algorithms Workflow workflow scheduling
Title	Evolutionary Multi-Objective Workflow Scheduling in Cloud
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