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Cloud-based serverless computing is an increasingly popular computingparadigm. In this paradigm, different services have diverse computingrequirements that justify deploying an inconsistently Heterogeneous Computing(HC) system to efficiently process them. In an inconsistently HC system, eachtask needed for a given service, potentially exhibits different execution timeson each type of machine. An ideal resource allocation system must be aware ofsuch uncertainties in execution times and be robust against them, so thatQuality of Service (QoS) requirements of users are met. This research aims tomaximize the robustness of an HC system utilized to offer a serverlesscomputing system, particularly when the system is oversubscribed. Our strategyto maximize robustness is to develop a task pruning mechanism that can be addedto existing task-mapping heuristics without altering them. Pruning tasks with alow probability of meeting their deadlines improves the likelihood of othertasks meeting their deadlines, thereby increasing system robustness and overallQoS. To evaluate the impact of the pruning mechanism, we examine it on variousconfigurations of heterogeneous and homogeneous computing systems. Evaluationresults indicate a considerable improvement (up to 35%) in the systemrobustness.

Improving Robustness of Heterogeneous Serverless Computing Systems Via Probabilistic Task Pruning