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Workflow scheduling is a long-studied problem in parallel and distributedcomputing (PDC), aiming to efficiently utilize compute resources to meet user'sservice requirements. Recently proposed scheduling methods leverage the lowresponse times of edge computing platforms to optimize application Quality ofService (QoS). However, scheduling workflow applications in mobile edge-cloudsystems is challenging due to computational heterogeneity, changing latenciesof mobile devices and the volatile nature of workload resource requirements. Toovercome these difficulties, it is essential, but at the same time challenging,to develop a long-sighted optimization scheme that efficiently models the QoSobjectives. In this work, we propose MCDS: Monte Carlo Learning using DeepSurrogate Models to efficiently schedule workflow applications in mobileedge-cloud computing systems. MCDS is an Artificial Intelligence (AI) basedscheduling approach that uses a tree-based search strategy and a deep neuralnetwork-based surrogate model to estimate the long-term QoS impact of immediateactions for robust optimization of scheduling decisions. Experiments onphysical and simulated edge-cloud testbeds show that MCDS can improve over thestate-of-the-art methods in terms of energy consumption, response time, SLAviolations and cost by at least 6.13, 4.56, 45.09 and 30.71 percentrespectively.

MCDS: AI Augmented Workflow Scheduling in Mobile Edge Cloud Computing Systems