Novel technique for efficient resource allocation in cloud server

Abstract

The cloud environment system is confronted with the issue of newlineimbalanced resource consumption as a result of unequal user demands. when newlinethe criteria is met, the cloud system s task scheduler efficiently schedules the newlinework and maintains the cloud system s balance. this thesis seeks to assess the newlinetask scheduling issues raised by modern heuristic algorithms, as well as to newlinediscuss the development of new techniques for addressing work scheduling newlineissues while maintaining a balanced resource usage. with different newlineperformance measures such as computation time, resource usage, cost, and newlineenergy efficiency, the presented methods attempt to handle the challenges of newlinetask scheduling in cloud computing environment. a number of task scheduling algorithms, such as the genetic based virus optimization algorithm (gbvoa), harris hawks optimization newlinecatalysed by simulated annealing (hho-csa), and harries hawks optimization and pigeon inspired optimization (hho-pio), were developed with these critical parameters in mind. the main idea is to achieve the shortest time to respond, maximum resource utilisation, and high energy efficiencyamong the cloud computing environment s resources. first, a genetic based virus optimization algorithm (gbvoa) fortask scheduling is conceptualized in a cloud computing environment. The spirit of the viral replication and genetic replication is inspirational in newlinedesigning the algorithm. different existing nature inspired task scheduling newlinealgorithms like genetic algorithm (ga), particle swarm optimization (pso) newlineand bee colony optimization (bco) were studied and found that it focused newlineon different parameters in scheduling the tasks. the motivation for choosing newlinethe virus replication and genetic approach is due to its biological nature. newline newline