EFFICIENT RESOURCE ALLOCATION IN HETEROGENEOUS CLOUD USING GENETIC ALGORITHMS AND CONTAINERIZATION

Abstract

The difficult task of resource configuration optimization for microservice management in heterogeneous cloud systems is addressed in this study. Resource utilization and network communication overhead, two crucial aspects of resource allocation, are used to guide the development of an improved framework, the multi-objective microservice allocation (MOMA) algorithm, which formulates the effective resource management of cloud microservice resources as a constrained optimization problem. Because workloads are dynamic and resource demands vary, it is still very difficult to allocate resources efficiently in container-based heterogeneous cloud environments. In this research, a novel method for resource allocation optimization using a Genetic Algorithm (GA) in such contexts is presented. Through the intelligent distribution of network, storage, and compute resources among several heterogeneous nodes, the suggested approach seeks to improve the performance, scalability, and dependability of cloud services. The GA dynamically creates solutions that balance load distribution, limit energy consumption, and maximize resource usage by modelling the resource allocation problem as a multi-objective optimization task. Simulation findings illustrate the usefulness of the suggested strategy, showing that it can surpass conventional allocation methods in terms of system stability and efficiency