Dynamic Task Offloading in Edge Computing Based on Dependency-Aware Reinforcement Learning

Abstract

Collaborative edge computing (CEC) is an emerging computing paradigm in which edge nodes collaborate to perform tasks from end devices. Task offloading decides when and at which edge node tasks are executed. Most existing studies assume task profiles and network conditions are known in advance, which can hardly adapt to dynamic real-world computation environments. Some learning-based methods use online task offloading without considering task dependency and network flow scheduling, leading to underutilized resources and flow congestion. We study Online Dependent Task Offloading (ODTO) in CEC, jointly optimizing network flow scheduling to optimize quality of service by reducing task completion time and energy consumption. The challenge of ODTO lies in how to offload dependent tasks and schedule network flows in dynamic networks. We model ODTO as the Markov Decision Process (MDP) and propose an Asynchronous Deep Progressive Reinforcement Learning (ADPRL) approach that optimize offloading and bandwidth decisions. We design a novel dependency-aware reward mechanism to address task dependency and dynamic network. Extensive experiments on the Alibaba cluster trace dataset and synthetic dataset indicate that our algorithm outperforms heuristic and learning-based methods in average task completion time and energy consumption.