ACO-Inspired Load Balancing Strategy for Cloud-Based Data Centre with Predictive Machine Learning Approach  

作　　者：Niladri Dey T.Gunasekhar K.Purnachand 

机构地区：[1]Department of CSE,K.L.University,Vaddeswaraam,522302,Andhra Pradesh,India [2]Department of CSE(Data Science),B.V.Raju Institute of Technology,TS,502313,India

出　　处：《Computers, Materials & Continua》2023年第4期513-529,共17页计算机、材料和连续体（英文）

摘　　要：Virtual Machines are the core of cloud computing and are utilized toget the benefits of cloud computing. Other essential features include portability,recovery after failure, and, most importantly, creating the core mechanismfor load balancing. Several study results have been reported in enhancing loadbalancingsystems employing stochastic or biogenetic optimization methods.It examines the underlying issues with load balancing and the limitationsof present load balance genetic optimization approaches. They are criticizedfor using higher-order probability distributions, more complicated solutionsearch spaces, and adding factors to improve decision-making skills. Thus, thispaper explores the possibility of summarizing load characteristics. Second,this study offers an improved prediction technique for pheromone level predictionover other typical genetic optimization methods during load balancing.It also uses web-based third-party cloud service providers to test and validatethe principles provided in this study. It also reduces VM migrations, timecomplexity, and service level agreements compared to other parallel standardapproaches.

关 键 词：Predictive load estimation load characteristics summarization correlation-based parametric reduction corrective coefficient-based 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]