Learning Workflow Scheduling on Multi-Resource Clusters

Abstract

Workflow scheduling is one of the key issues in the management of workflow execution. Typically, a workflow application can be modeled as a Directed-Acyclic Graph (DAG). In this paper, we present GoDAG, an approach that can learn to well schedule workflows on multi-resource clusters. GoDAG directly learns the scheduling policy from experience through deep reinforcement learning. In order to adapt deep reinforcement learning methods, we propose a novel state representation, a practical action space and a corresponding reward definition for workflow scheduling problem. We implement a GoDAG prototype and a simulator to simulate task running on multiresource clusters. In the evaluation, we compare the GoDAG with three state-of-the-art heuristics. The results show that GoDAG outperforms the baseline heuristics, leading to less average makespan to different workflow structures.

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“…One other work used A3C (Asynchronous Advantage Actor-Critic) and residual neural network for scheduling stochastic edge-cloud environment [239]. some work also used the same RL model for workflow scheduling [240,241].…”

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