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

作　　者：Nidhika Chauhan Navneet Kaur Kamaljit Singh Saini Sahil Verma Kavita Ruba Abu Khurma Pedro A.Castillo 

机构地区：[1]University Institute of Computing Department,Chandigarh University,Punjab,140413,India [2]Department of Computer Science and Engineering,Chandigarh University,Punjab,140413,India [3]Universidade Federal do Piauí,Teresina,Piauí,64049-550,Brazil [4]MEU Research Unit,Faculty of Information Technology,Middle East University,Amman,11831,Jordan [5]Applied Science Research Center,Applied Science Private University,Amman,11931,Jordan [6]Department of Computer Engineering,Automatics and Robotics,University of Granada,Granada,18071,Spain

出　　处：《Computers, Materials & Continua》2024年第6期3757-3782,共26页计算机、材料和连续体（英文）

基　　金：supported by the Ministerio Espanol de Ciencia e Innovación under Project Number PID2020-115570GB-C22 MCIN/AEI/10.13039/501100011033 and by the Cátedra de Empresa Tecnología para las Personas(UGR-Fujitsu).

摘　　要：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 Algo-rithm.Our findings reveal that our proposed algorithm,Knowledge Based Flower Pollination Algorithm(KB-FPA),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.

关 键 词：Cloud computing resource allocation energy consumption optimization algorithm flower pollination algorithm 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]