Deep reinforcement learning for workflow optimization using provenance-based simulation

Abstract

Deep reinforcement learning techniques and provenance-based simulation are employed for resource allocation in a shared computing environment. One method comprises: obtaining a specification of a workflow of concurrent workflows in a shared computing environment, wherein the specification comprises workflow states and one or more control variables for the workflow in the shared computing environment; obtaining a simulation model of the workflow representing different configurations of the control variables; evaluating the control variables for the concurrent workflows using a reinforcement learning (RL) agent by observing the states and obtaining an expected utility score for control variable combinations for the execution of the concurrent workflows given an allocation of resources of the shared computing environment, wherein the RL agent performs, using the simulation model, the evaluating, the obtaining the expected utility score, and/or a training of an RL model; and providing an allocation of the resources based on the expected utility score.