Computation Offloading and Resource Allocation in IoT-Based Mobile Edge Computing Systems
Bintao Hu, Yuan Gao, Wenzhang Zhang, Dongyao Jia, Hengyan Liu
Abstract
With the rise in popularity of artificial intelligence (AI) and internet of things (IoT) technologies, advanced AI technologies have been widely applied to support delay/time-sensitive tasks of IoT-based user equipment (UE) in IoT systems, which allows IoT-based UEs to offload their tasks to a remote fog, edge or cloud computing server. To reduce the consumption of delays (which may include transmission delays, queueing delays, and processing delays) while efficiently allocating the computation resource at a remote server, an efficient offloading decision solution needs to be proposed. In this paper, an IoT-based network system consisting of two layers will be proposed, where The bottom layer is the IoT-based UE layer, which includes multiple IoT-based UEs, and the top layer is the mobile edge computing (MEC) layer, which includes an edge node embedded with the base station. We propose a double Q-Learning-based offloading decision and computation resource allocation optimisation algorithm (DQOCA), which aims to jointly optimise the offloading decisions among all IoT-based UEs and optimise computation resource at the MEC server to reduce the maximum delay consumption among all IoT-based UEs. Simulation findings show that, in comparison to benchmarks (i.e., local processing and edge processing schemes), our proposed approach greatly minimises the maximum delay consumption.