Journals Proceedings

Abstract

Resource management and job scheduling are two problems that go hand-in-hand and the solutions to which are primarily dependent on the nature of workload. With increasing demand to automate the entire process from allocating resources to scheduling jobs efficiently, deep reinforcement learning techniques have been brought into the picture which adapt to the environment and learn from experience. In this paper, we present SchedQRM which classifies burst time of jobs based on their signature and employs Deep Q-Network algorithm to find an optimal solution for any arbitrary job set. We also evaluate our proposed work against state-of-the-art heuristics to show the efficacy of our approach.