Evolutionary Multi-Objective Workflow Scheduling in Cloud

Evolutionary Multi-Objective Workflow Scheduling in Cloud

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 make span and cost as a Multi-objective Optimization Problem < Final Year Projects 2016 > 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.