Maximizing Resource Efficiency in Cloud Data Centers through Knowledge-Based Flower Pollination Algorithm (KB-FPA)

Abstract

Cloud computing is a dynamic and rapidly evolving field, where the demand for resources fluctuates continuously. This paper delves into the imperative need for adaptability in the allocation of resources to applications and services within cloud computing environments. The motivation stems from the pressing issue of accommodating fluctuating levels of user demand efficiently. By adhering to the proposed resource allocation method, we aim to achieve a substantial reduction in energy consumption. This reduction hinges on the precise and efficient allocation of resources to the tasks that require those most, aligning with the broader goal of sustainable and eco-friendly cloud computing systems. To enhance the resource allocation process, we introduce a novel knowledge-based optimization algorithm. In this study, we rigorously evaluate its efficacy by comparing it to existing algorithms, including the Flower Pollination Algorithm (FPA), Spark Lion Whale Optimization (SLWO), and Firefly Algorithm. Our findings reveal that our proposed algorithm, Knowledge Based Flower Pollination Algorithm (KBFPA), consistently outperforms these conventional methods in both resource allocation efficiency and energy consumption reduction. This paper underscores the profound significance of resource allocation in the realm of cloud computing. By addressing the critical issue of adaptability and energy efficiency, it lays the groundwork for a more sustainable future in cloud computing systems. Our contribution to the field lies in the introduction of a new resource allocation strategy, offering the potential for significantly improved efficiency and sustainability within cloud computing infrastructures.