Research on Resource Scheduling Optimization Algorithms for Cloud Platform Business Systems in a Microservices Architecture

This paper proposes a new framework called Markov Decision Genetics (MDG). This framework combines microservice architecture, Markov Decision Process (MDP) and Genetic Algorithm (GA) to solve the problem of resource scheduling optimization of cloud platform business system under microservice architecture. MDG framework formalizes resource scheduling problems through MDP and automatically optimizes resource allocation strategies through GA. This method can intelligently predict and adapt to the changes of system load, so as to realize efficient and dynamic resource scheduling. We have built a system architecture based on IaaS, PaaS, SaaS three-layer cloud service model, which provides comprehensive support for the research of resource scheduling optimization algorithm of cloud platform business system under microservice architecture. Finally, this paper verifies the significant advantages of the proposed cloud-native architecture based business system resource scheduling optimization algorithm in terms of accuracy, scientific decision making and overall efficiency improvement by comparing the model algorithm with the neural network algorithm. The results show that the resource scheduling optimization algorithm can effectively solve the complexity and uncertainty problems that the traditional scheduling methods are difficult to deal with, and also prove the effectiveness of the model in the optimization and feasibility of business system resource scheduling.