PSRL: A New Method for Real-Time Task Placement and Scheduling Using Reinforcement Learning

Abstract

Modern real-time system development methodologies describe a stage in which application tasks are deployed onto an execution platform. The deployment process is divided into two steps: (i) task placement on processors and (ii) task scheduling to determine their execution order. The overall performance of the deployment model depends on the two steps, which are interdependent. In this paper, a new method based on reinforcement learning techniques, called PSRL, is proposed. PSRL explores all the feasible placements in the first step. In the second step, an optimal schedule is considered for each feasible placement. PSRL generates the optimal deployment, which corresponds to the placement and scheduling that minimize task response times. Application to case studies shows the applicability and quality of the obtained solutions when compared to related work.