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Evolutionary Multi-Objective Workflow Scheduling in Cloud

Evolutionary Multi-Objective Workflow Scheduling in Cloud

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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 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 dif?culties, and model the work?ow scheduling problem which optimizes both make span 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 problems pecificen coding 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.


 


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  • Model: PROJ5697
  • 999 Units in Stock
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This product was added to our catalog on Friday 07 October, 2016.

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