Multi-Task Computation Offloading Based On Evolutionary Multi-Objective Optimization in Industrial Internet of Things

The development of Industrial Internet of Things (IIoT) has completely changed traditional manufacturing industry. Industrial equipments with limited resources often cannot meet the diverse demands of numerous computing-intensive and latency-sensitive tasks. Mobile Edge Computing (MEC) offloads these tasks to nearby edge servers to achieve lower latency and energy consumption. However, considering the channel interferences of the network and the diverse demands of different tasks, coordinating computation offloading among multiple devices is challenging. To address this challenge, the computation offloading is formulated as a multi-objective optimization problem, and a new task model composed of scientific workflow tasks and concurrency workflow tasks is proposed to represent the multi-task in the industrial environment. In addition, a two-hierarchical optimization framework is devised to optimize the bandwidth allocation and the multi-task computation offloading through the dynamic bandwidth pre-allocation and the improved multi-objective evolutionary algorithm based on decomposition with two performance enhancing schemes. Comprehensive experiments demonstrate that the effectiveness and efficiency of our proposed framework in terms of the trade-offs between latency and energy consumption, as well as the convergence and diversity of obtained non-dominated solutions.