Collaborative Optimization Scheduling of Cloud Service Resources Based on Improved Genetic Algorithm

With the wide application of cloud computing technology, the services provided by cloud systems have become increasingly diverse, thus these systems are required to solve tasks of high variety and complexity, with a tremendously extensive amount of task data involved. That is why reasonable scheduling system resources are particularly important in cloud computing research. In this paper, a cloud computing system needs to take into account a wider range of cloud service resource types and collaborative optimization scheduling issues in order to solve the tasks at hand. Firstly, a new adaptive genetic algorithm (NAGA) was proposed. By improving the crossover mutation genetic operator, the algorithm was able to save excellent individuals as much as possible, enhance the algorithm's optimization ability, and greatly reduce the probability of the algorithm falling into the local optimal solution. Secondly, focusing on the main factors affecting service quality, such as task completion time, system load, and network bandwidth, an upgraded fitness operator method for the cloud resource collaborative optimization scheduling problem is set forth. Finally, an algorithm of cloud service resources based on an improved genetic algorithm (OSIG) is proposed. Experiments on the CloudSim cloud computing simulation platform demonstrate that the OSIG algorithm proposed in this paper can effectively optimize the resource scheduling strategy, shorten the task completion time, facilitate the system load balancing, and boost the system's service quality. The theoretical analysis was consistent with the experimental results.