Compute First Networking Scheduling Strategy Based on Genetic Algorithm in Edge Computing Environment

In recent years, the research of distributed generation, especially renewable energy sources, have received unprecedented attention all over the world. The proportion of distributed new energy grid absorbed is increasing year by year, and the demand of smart grid for computing resources is diverse and dynamic. The mismatch between the existing computing communication architecture and the dynamic computing resource requirements of distributed business has become an important technical problem to be solved for the further intelligence of distribution network in the dual-carbon background. In this article, we propose a triple fitness genetic algorithm (TFGA) for make fast and accurate computing scheduling. Specifically, we first introduce a compute first networking scheduling framework in the edge cloud computing environment. Second, we improve task scheduling strategy in cloud computing by add two fitness in genetic algorithm, and maximize the efficiency of cloud computing environment by optimizing task scheduling, so as to make fast and accurate arithmetic scheduling. Subsequently, we conducted a comparison of simulation experiments between the Adaptive Genetic Algorithm (AGA) and TFGA, under identical environmental conditions. Extensive simulation results show that the system can effectively assist smart grid in rational arithmetic dispatch and improving resource utilization efficiency.