Optimal Resource Allocation Using Genetic Algorithm in Container-Based Heterogeneous Cloud

Abstract

This paper tackles the complex problem of optimizing resource configuration for microservice management in heterogeneous cloud environments. To address this challenge, an enhanced framework, the multi-objective microservice allocation (MOMA) algorithm, is developed to formulate the efficient resource management of cloud microservice resources as a constrained optimization problem, guided by resource utilization and network communication overhead, which are two important factors in microservice resource allocation. The proposed framework simplifies the deployment of cloud services and streamlines workload monitoring and analysis within a diverse cloud system. A comprehensive comparison is made between the effectiveness of the proposed algorithm and existing algorithms on real-world datasets, with a focus on resource balancing, network overhead, and network reliability. Experimental results reveal that the proposed algorithm significantly enhances resource utilization, reduces network transmission overhead, and improves reliability.

Keywords

• Resource allocation
• genetic algorithm
• container-based heterogeneous cloud
• multi-objective optimization
• microservice