PROJECT TITLE :
Evolutionary Multi-Objective Workflow Scheduling in Cloud
Cloud computing provides promising platforms for executing giant applications with monumental computational resources to supply on demand. In a very Cloud model, users are charged based mostly on their usage of resources and the specified quality of service (QoS) specifications. Although there are various existing workflow scheduling algorithms in ancient distributed or heterogeneous computing environments, they have difficulties in being directly applied to the Cloud environments since Cloud differs from ancient heterogeneous environments by its service-based mostly resource managing technique and pay-per-use pricing methods. During this paper, we have a tendency to 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 tend to propose an evolutionary multi-objective optimization (EMO)-based mostly algorithm to solve this workflow scheduling problem on an infrastructure as a service (IaaS) platform. Novel schemes for downside-specific encoding and population initialization, fitness evaluation and genetic operators are proposed during this algorithm. Extensive experiments on globe workflows and randomly generated workflows show that the schedules created by our evolutionary algorithm present a lot of stability on most of the workflows with the instance-primarily based IaaS computing and pricing models. The results conjointly show that our algorithm can achieve considerably better solutions than existing state-of-the-art QoS optimization scheduling algorithms in most cases. The conducted experiments are primarily based on the on-demand instance sorts of Amazon EC2; but, the proposed algorithm are straightforward to be extended to the resources and pricing models of different IaaS services.
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