Balanced multi-access edge computing offloading strategy in the Internet of things scenario

Internet of things devices can offload part of the tasks to the edge servers through the wireless network, thus the computing pressure and energy consumption are reduced. However, this will increase the cost of communication. When designing the offloading strategy for the edge computing scenario of the Internet of things, it is necessary to maintain the balance between task execution energy and experiment. Therefore, this paper proposes an offloading strategy which can optimize the energy consumption and time delay of task execution at the same time. This strategy satisfies different preferences of users. First, the above task is modeled as a multi-objective optimization problem, and the Pareto solution set is found by improving the strength Pareto evolutionary algorithm (SPEA2). Based on the Pareto set, the offloading strategy satisfying the requires of users with different preferences by offloading cost estimation. Second, a simulation experiment is carried out to verify the robustness of the improved SPEA2 algorithm under the influence of different main parameters. Compared with other representative algorithms, the improved SPEA2 algorithm is proved to minimize the task execution delay and energy consumption jointly.