Research on Adaptive Network Load Balancing Algorithm under Cloud Edge Collaboration

With the development of IoT technology and the popularization of wireless networks, the number of mobile user devices is increasing, and there is a higher demand for performance indicators such as delay, energy consumption and throughput. The traditional cloud computing centralized processing model is difficult to meet the user's high performance requirements, as a result, this paper proposes the study of adaptive network load balancing algorithm under cloud-side collaboration. First, an adaptive genetic algorithm is used to formulate the load balancing and subsequent update operations. Specifically, the original problem is decomposed into two subproblems, power allocation and computational resource allocation, under each load balancing decision update; then, according to the theory of convex optimization and quasi-convex optimization, the optimal solutions for power allocation and computational resource allocation are derived using the bifurcated search method and the Lagrange multiplier method, respectively. Simulation results show that the proposed scheme reduces the total user overhead while guaranteeing the user delay constraint, which effectively improves the performance of the system and the quality of user experience.