Ant Colony Algorithm for Multi-Objective Optimization of Container-Based Microservice Scheduling in Cloud

Abstract

In cloud architectures, the microservice model divides an application into a set of loosely coupled and collaborative fine-grained services. As a lightweight virtualization technology, the container supports the encapsulation and deployment of microservice applications. Despite a large number of solutions and implementations, there remain open issues that have not been completely addressed in the deployment and management of the microservice containers. An effective method for container resource scheduling not only satisfies the service requirements of users but also reduces the running overhead and ensures the performance of the cluster. In this paper, a multi-objective optimization model for the container-based microservice scheduling is established, and an ant colony algorithm is proposed to solve the scheduling problem. Our algorithm considers not only the utilization of computing and storage resources of the physical nodes but also the number of microservice requests and the failure rate of the physical nodes. Our algorithm uses the quality evaluation function of the feasible solutions to ensure the validity of pheromone updating and combines multi-objective heuristic information to improve the selection probability of the optimal path. By comparing with other related algorithms, the experimental results show that the proposed optimization algorithm achieves better results in the optimization of cluster service reliability, cluster load balancing, and network transmission overhead.