DYNAMIC RESOURCE ALLOCATION IN CLOUD COMPUTING: ALGORITHMS AND PERFORMANCE METRICS

Abstract

     In the rapidly evolving panorama of cloud computing, green aid allocation is crucial to optimize machine overall performance, manipulate fees, and meet the various desires of users. Dynamic Resource Allocation (DRA) algorithms play a central role in ensuring that cloud assets are allotted efficiently, adapting to fluctuating call for whilst preserving service great. This article gives a comprehensive exam of diverse DRA algorithms, such as heuristic, metaheuristic, and machine studying-based totally approaches, and assesses their effectiveness through key overall performance metrics. We analyze the algorithms’ strengths and limitations concerning elements together with scalability, useful resource utilization, response time, and power efficiency. Additionally, we discover the demanding situations and destiny traits in DRA, emphasizing the effect of real-time statistics analytics and predictive modeling in enhancing allocation strategies. By evaluating the interaction among algorithms and overall performance metrics, this look at aims to provide treasured insights for researchers and practitioners striving to improve cloud computing efficiency and reliability. Furthermore, we speak the combination of emerging technologies, inclusive of 5G and edge computing, and their capability have an effect on on dynamic aid allocation mechanisms. The article additionally highlights real-international case studies and applications, showcasing the realistic implementation of DRA algorithms in various cloud environments. Key considerations including load balancing, fault tolerance, and fine of service are addressed to offer a holistic view of the resource allocation method. Through those insights, the thing objectives to manual destiny studies efforts and foster innovation in the cloud computing area.