A Mobility-Aware and Fault-Tolerant Service Offloading Method in Mobile Edge Computing

Abstract

Mobile edge computing (MEC) is a prospective technology to render services through resources to fulfill the requirements of IoT (Internet of Things) devices at the cloud edge. The highly dynamic and heterogeneous characteristics of IoT devices bring both opportunities and challenges, i.e., a higher-than-usual occurrence rate of failures. Such failures occur at all architectural levels of the IoT applications: IoT sensor and actuator nodes can be missed, network links between IoT nodes can be down, and processing and storage IoT components can fail. In this work, for optimizing service offloading efficiency, energy consumption, and system reliability, a semi-online fault-tolerant offloading method (UDQF) was proposed for countering MEC failures by adopting a semi-online-learning-based service offloading strategy. The proposed strategy leverages a Dueling Deep Q network-based algorithm to determine user offloading behavior and utilizes an adaptive checkpointing mechanism (periodically storing the system state and restarting the system at the last checkpointing) to improve the task reliability. To valid and compare the model, the simulated results indicate that the proposed method outperforms other counterparts in multiple metrics.