Abstract
Knowledge-Driven Multi-Objective Evolutionary Scheduling Algorithm for Cloud Workflows
Highlights
W ITH the advantages of virtualization, rapid elasticity, on-demand access, the economy of scale, and so forth, cloud computing has proliferated rapidly over the past decade [1]
Powerful scheduling approaches satisfy the user-specified quality of service (QoS), and substantially improve the performance of cloud platforms
1) Based on the knowledge of workflow structure, we tailor a new decision variable grouping strategy to accelerate the convergence speed of MOEAs in solving multi-objective workflow scheduling problems; 2) We mine the knowledge of workflow tasks and cloud resources to estimate the ideal and nadir points for objective space normalization, to maintain population diversity for MOEAs; 3) Extensive comparison experiments are conducted on realworld workflows to analyze the performance of the proposal
Summary
W ITH the advantages of virtualization, rapid elasticity, on-demand access, the economy of scale, and so forth, cloud computing has proliferated rapidly over the past decade [1]. Li et al [19] proposed a new multi-objective workflow scheduling algorithm, called SDHN, to balance the makespan and cost of workflow execution in cloud platforms. The heterogeneity and elasticity of cloud resources substantially expand the solution space, which further challenges the MOEAs. In addition, the optimization objectives of makespan and monetary cost in workflow scheduling are of entirely different scales. 1) Based on the knowledge of workflow structure, we tailor a new decision variable grouping strategy to accelerate the convergence speed of MOEAs in solving multi-objective workflow scheduling problems; 2) We mine the knowledge of workflow tasks and cloud resources to estimate the ideal and nadir points for objective space normalization, to maintain population diversity for MOEAs; 3) Extensive comparison experiments are conducted on realworld workflows to analyze the performance of the proposal.
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