Cost-Minimized Microservice Migration With Autoencoder-Assisted Evolution in Hybrid Cloud and Edge Computing Systems

Hybrid cloud-edge systems combine the advantages of cloud computing and mobile edge computing (MEC) to achieve flexible integration and fluidity of data between the cloud and the edge. To address dynamic and stochastic loads caused by mobile users (MUs) and time-varying tasks, MEC network operators need to continuously migrate installed services among edge servers, significantly increasing network maintenance costs. Existing studies often overlook the service migration cost resulting from MU mobility. Therefore, we present a joint optimization scheme focusing on minimizing the operational cost of hybrid cloud-edge systems while considering the dynamic service migration cost induced by MUs. With the rapid development of 5G/6G technologies, many MUs require connectivity to edge nodes (ENs) or cloud data centers (CDCs) for processing. Minimizing the operational cost of hybrid cloud-edge systems while considering many heterogeneous decision variables is a challenge. To solve this complex high-dimensional mixed-integer nonlinear problem, we develop a novel deep learning-based evolutionary algorithm called autoencoder-based multiswarm gray wolf optimizer based on genetic learning (AMGG). Experimental results with real data demonstrate that AMGG achieves lower system cost by 49.69% while strictly meeting task latency requirements of MUs compared with state-of-the-art algorithms.