Reliable and efficient computation offloading for dependency-aware tasks in IIoT using evolutionary multi-objective optimization

Abstract

Mobile Edge Computing (MEC) significantly enhances the computing and processing capabilities of Industrial Internet of Things (IIoT) systems with hardware resource constraints. However, the complexity of industrial environments and the dependencies between tasks in industrial applications pose new challenges for collaborative edge computing offloading between IIoT devices and MEC systems. In industrial applications, complex workflow tasks can be modeled using Directed Acyclic Graphs (DAGs). The execution order of each task and the offloading decisions within the workflow can result in varying completion times, ultimately affecting the overall Quality of Experience (QoE) of the workflow. Therefore, it is crucial to pay attention to the execution order of tasks and offloading decisions within workflows in MEC-assisted industrial systems. This paper introduces an enhanced multi-objective evolutionary algorithm designed to minimize both the completion time of industrial applications and the energy usage of IIoT devices. Firstly, a retransmission model is incorporated to simulate the phenomenon caused by packet loss in complex industrial environments. Then, a dynamic task scheduling algorithm based on response rate and a delay-based execution position initialization method are designed to achieve optimal scheduling and offloading for DAG applications. Finally, to prevent task failures due to retransmission delays, two optimization strategies for task retransmission mechanisms are proposed. Comprehensive experimental results demonstrate that, under the same computational offloading scenarios, the proposed algorithm achieves better convergence and diversity of non-dominated solutions,while significantly reducing system latency and energy consumption.