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.
sales on Site11,021