RESOURCE ALLOCATION IN CLOUD COMPUTING USING A GENERALIZED KNAPSACK ALGORITHM

Abstract

Efficient allocation of resources in order to achieve optimal performance and cost-effectiveness is a critical challenge in cloud
computing. This paper presents the Generalized Knapsack Algorithm (GKA) in order to address the resource allocation problem in
cloud environments. The GKA aims to maximize the utilization of computing resources, memory, and bandwidth while considering
various constraints. The paper presents a comprehensive analysis of the GKA's performance using both simulated experiments and real-world cloud datasets. Results demonstrate that the GKA outperforms existing resource allocation methods in terms of efficiency and scalability. The proposed approach provides a promising solution for enhancing resource allocation strategies in cloud computing, enabling better resource utilization and improved service delivery for cloud users. The study contributes to the advancement of cloud computing optimization and has practical implications for cloud service providers and users, fostering more effective resource management in cloud environments.