Evolutionary Computational Offloading with Autoencoder in Large-scale Edge Computing

Cloud-edge hybrid systems can support delay-sensitive applications of industrial Internet of Things. Edge nodes (ENs) as service providers, provide users computing/network services in a pay-as-you-go manner, and they also suffer from the high cost brought by providing computing resources. Thus, the problem of profit maximization is highly important to ENs. However, with the development of 5G network technologies, a large number of mobile devices (MDs) are connected to ENs, making the above-mentioned problem a high-dimensional challenge, which is highly difficult to solve. This work formulates a joint optimization problem of task offloading, task partitioning, and associations of large-scale users to ENs to maximize the profit of ENs. This work focuses on applications that can be split into multiple subtasks, each of which can be completed in MDs, ENs and a cloud data center. Specifically, a mixed integer nonlinear program is formulated to maximize ENs' profit. Then, a novel hybrid algorithm named Genetic Simulated-annealing-based Particle swarm optimizer with a Stacked Autoencoder (GSPSA) is designed to solve it. Real-life data-based experimental results demonstrate that compared with other peer algorithms, GSPSA increases the profit of ENs while strictly meeting latency needs of users' tasks. The dimension of the problem that can be solved is increased by more than 50% with GSPSA.