Deep Reinforcement Learning for Online Latency Aware Workload Offloading in Mobile Edge Computing

TL;DR

This paper introduces DECENT, a deep reinforcement learning algorithm that optimizes task offloading and resource management in mobile edge computing to minimize response time, considering queue delays and task priorities.

Contribution

It proposes a novel DRL-based approach, DECENT, for joint offloading and resource allocation in MEC, addressing queue delays and task priorities for the first time.

Findings

DECENT reduces response time significantly in experiments.

The algorithm effectively balances load and resource allocation.

It outperforms existing offloading strategies.

Abstract

Owing to the resource-constrained feature of Internet of Things (IoT) devices, offloading tasks from IoT devices to the nearby mobile edge computing (MEC) servers can not only save the energy of IoT devices but also reduce the response time of executing the tasks. However, offloading a task to the nearest MEC server may not be the optimal solution due to the limited computing resources of the MEC server. Thus, jointly optimizing the offloading decision and resource management is critical, but yet to be explored. Here, offloading decision refers to where to offload a task and resource management implies how much computing resource in an MEC server is allocated to a task. By considering the waiting time of a task in the communication and computing queues (which are ignored by most of the existing works) as well as tasks priorities, we propose the \ul{D}eep reinforcement l\ul{E}arning based offloading de\ul{C}ision and r\ul{E}source manageme\ul{NT} (DECENT) algorithm, which leverages the advantage actor critic method to optimize the offloading decision and computing resource allocation for each arriving task in real-time such that the cumulative weighted response time can be minimized. The performance of DECENT is demonstrated via different experiments.