Abstract
Edge nodes (ENs) in mobile edge computing can support current delay-sensitive applications of the Industrial Internet of Things. ENs are deployed in the network edge and can execute computational tasks offloaded from users' mobile devices (MDs) in a timely way. However, their computing and communication resources are limited and cannot execute all offloaded tasks. Thus, a cloud data center (CDC) is highly needed and hybrid cloud-edge systems emerge to provide low-delay services. This work investigates a joint optimization problem of task offloading, task partitioning, and user association to minimize the total cost of the system. This work focuses on applications that can be split into multiple dependent subtasks, each of which can be completed in MDs, ENs, and CDC. Specifically, a mixed integer nonlinear program is formulated to minimize the total cost. Then, a hybrid algorithm named Genetic Simulated-annealing-based Particle Swarm Optimizer (GSPSO) is designed to solve it. GSPSO yields a close-to-optimal strategy to jointly optimize connections among MDs and ENs, and allocation ratios among MDs, ENs, and CDC. Experimental results demonstrate that compared with benchmark methods, GSPSO decreases the total cost while fully meeting the completion time requirements of user tasks.