Ensuring truthfulness for scheduling multi-objective real time tasks in multi cloud environments

M. Geethanjali, J. Angela, Jennifa Sujana, T. Revathi
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引用次数: 9

Abstract

Cloud computing provides dynamic provisioning for real time applications over the Internet. These services are accessed by number of clients as pay per use over the internet. In this scenario, scheduling the current jobs to be executed with given constraints for the real time tasks is an essential requirement. Hence task scheduling is a major challenge in cloud computing. In general, the main aim of Cloud Service Providers (CSPs) is to earn more amount of revenue. So, the providers may provide false information about their resources to gain more profit. To enforce the genuineness of information, game theory model is used. In older approaches, a scheduling algorithm is used to schedule the task with maximum estimated gain and executes the tasks in the queue. Therefore it increases the execution time of the task. This paper presents a scheduling mechanism for real time tasks to achieve timing constraint and minimum cost for the job execution. The game theory mechanism ensures that the truthful information is provided by CSPs. we found that the induced results of the proposed algorithm are effective and our simulation results outperform the traditional scheduling algorithms with multi-objective optimization.
确保多云环境下多目标实时任务调度的真实性
云计算通过Internet为实时应用程序提供动态供应。这些服务是通过互联网按每次使用付费的方式按客户数量访问的。在这个场景中,调度当前作业以执行给定的实时任务约束是一个基本需求。因此,任务调度是云计算中的一个主要挑战。一般来说,云服务提供商(csp)的主要目标是赚取更多的收入。因此,供应商可能会提供有关其资源的虚假信息以获得更多利润。为了保证信息的真实性,采用了博弈论模型。在旧的方法中,调度算法使用最大估计增益来调度任务,并执行队列中的任务。因此增加了任务的执行时间。提出了一种实时任务调度机制,以实现任务执行的时间约束和成本最小化。博弈论机制保证了csp提供的信息是真实的。仿真结果表明,该算法的仿真结果优于传统的多目标优化调度算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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