Genetic Algorithm-Based Optimal Resource Trust Line Prediction in Cloud Computing

Author:

Mercy S.1,Jaiganesh M.2,Nagaraja R.1,Sudha G.3

Affiliation:

1. Department of Information Science and Engineering, Bangalore Institute of Technology, Bangalore, Karnataka, India

2. Department of Information Technology, Karpagam College of Engineering, Coimbatore, Tamil Nadu, India

3. Department of Electrical and Electronics Engineering, Bangalore Institute of Technology, Bangalore, Karnataka, India

Abstract

A cloud computing signifies a novel computing paradigm that endorses reactive delivery of resources and services. A distinctive cloud service of such data center deploys over many computing nodes requesting services from the data centers. The organization of resources and trustworthiness of client is a hot topic of research in cloud computing. One of the major threats in cloud computing is unauthorized access of hardware and their resources. To conquer the issue, this novel work proposes an Optimal Resource Trust line prediction using Genetic Algorithm (GAORTL). The main aim of the work is to find the allocated optimal resource utilization of clients through an evolutionary algorithm. Implementation is evaluated to prove the benefit of the algorithm. Subsequently, we perform a comprehensive investigation that the proposed GAORTL delivers a better prediction of trustworthiness in variety of client sizes for a big scale batch of occurrences.

Publisher

World Scientific Pub Co Pte Ltd

Subject

Computer Science Applications,Theoretical Computer Science,Software

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Research on the hybrid chaos-coud salp swarm algorithm;Communications in Nonlinear Science and Numerical Simulation;2024-11

2. Guest Editorial — Introduction to the Special Issue on Smart Fuzzy Optimization for Decision-Making in Uncertain Environments;International Journal of Computational Intelligence and Applications;2023-03

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3