Task Graph offloading via Deep Reinforcement Learning in Mobile Edge Computing

TL;DR

This paper proposes a deep reinforcement learning approach to optimize task graph offloading in mobile edge computing environments, effectively adapting to dynamic conditions and improving user experience.

Contribution

It introduces SATA-DRL, a novel deep reinforcement learning algorithm that models task offloading as an MDP to adapt to environmental changes in MEC.

Findings

SATA-DRL outperforms existing strategies in reducing makespan.

SATA-DRL decreases deadline violations.

The approach adapts effectively to dynamic MEC environments.

Abstract

Various mobile applications that comprise dependent tasks are gaining widespread popularity and are increasingly complex. These applications often have low-latency requirements, resulting in a significant surge in demand for computing resources. With the emergence of mobile edge computing (MEC), it becomes the most significant issue to offload the application tasks onto small-scale devices deployed at the edge of the mobile network for obtaining a high-quality user experience. However, since the environment of MEC is dynamic, most existing works focusing on task graph offloading, which rely heavily on expert knowledge or accurate analytical models, fail to fully adapt to such environmental changes, resulting in the reduction of user experience. This paper investigates the task graph offloading in MEC, considering the time-varying computation capabilities of edge computing devices. To adapt to environmental changes, we model the task graph scheduling for computation offloading as a Markov Decision Process (MDP). Then, we design a deep reinforcement learning algorithm (SATA-DRL) to learn the task scheduling strategy from the interaction with the environment, to improve user experience. Extensive simulations validate that SATA-DRL is superior to existing strategies in terms of reducing average makespan and deadline violation.