Research on Microservice Workflow Scheduling Based on Hybrid Optimization Algorithm

With the development of microservice technology, business requirements are becoming more and more diversified. While users bring many benefits, it also brings new challenges to the optimization of workflow scheduling, in which cost and completion time are the most concerned issues. This paper focuses on how to find the optimal scheduling scheme to minimize the maximum completion time under budget cost constraints in a cloudy environment. To solve this problem, this paper proposes an improved immune particle swarm optimization algorithm by combining the inertia weight factor and learning factor optimization with the advantages of immune algorithm in vaccine inoculation, concentration mechanism and vaccine selection. It uses diversity to control the number of vaccines inoculated by the population and the scope of vaccination, which not only solves the premature phenomenon of particle swarm optimization algorithm due to lack of diversity, At the same time, the convergence speed and accuracy of the algorithm are improved. Compared with classical particle swarm optimization algorithm, classical immune algorithm and immune particle swarm opti-mization algorithm, experiments show that the proposed method is effective.