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Workflow decision making is critical to performing many practical workflowapplications. Scheduling in edge-cloud environments can address the highcomplexity problem of workflow applications, while decreasing the datatransmission delay between the cloud and end devices. However, because of theheterogeneous resources in edge-cloud environments and the complicated datadependencies among the tasks in a workflow, significant challenges for workflowscheduling remain, including the selection of an optimal tasks-servers solutionfrom the possible numerous combinations. The existing studies have been mainlydone subject to rigorous conditions without fluctuations, ignoring the factthat workflow scheduling is typically present in uncertain environments. Inthis study, we focus on reducing the execution cost of workflow applicationsmainly caused by task computation and data transmission, while satisfying theworkflow deadline in uncertain edge-cloud environments. The Triangular FuzzyNumbers (TFNs) are adopted to represent the task processing time and datatransferring time. A cost-driven fuzzy scheduling strategy based on an AdaptiveDiscrete Particle Swarm Optimization (ADPSO) algorithm is proposed, whichemploys the operators of Genetic Algorithm (GA). This strategy introduces therandomly two-point crossover operator, neighborhood mutation operator, andadaptive multipoint mutation operator of GA to effectively avoid converging onlocal optima. The experimental results show that our strategy can effectivelyreduce the workflow execution cost in uncertain edge-cloud environments,compared with other benchmark solutions.

A Cost-Driven Fuzzy Scheduling Strategy for Intelligent Workflow Decision Making Systems in Uncertain Edge-Cloud Environments