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

S. Mercy, M. Jaiganesh, R. Nagaraja, G. Sudha
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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.
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云计算中基于遗传算法的最优资源信任线预测
云计算意味着一种新的计算范式,它支持响应式地交付资源和服务。这种数据中心的独特云服务部署在许多从数据中心请求服务的计算节点上。资源的组织和客户端的可信赖性是云计算领域的研究热点。云计算的主要威胁之一是对硬件及其资源的未经授权访问。为了解决这个问题,本文提出了一种使用遗传算法(GAORTL)的最优资源信任线预测方法。该工作的主要目的是通过进化算法找到分配给客户端的最优资源利用率。最后对算法的实现进行了评价,证明了算法的有效性。随后,我们进行了一项全面的调查,表明所提出的GAORTL在各种客户规模的大规模事件中提供了更好的可信度预测。
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