• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Soft computing: A fusion of foundations, methodologies and applications >Online scheduling of dependent tasks of cloud's workflows to enhance resource utilization and reduce the makespan using multiple reinforcement learning-based agents
【24h】

Online scheduling of dependent tasks of cloud's workflows to enhance resource utilization and reduce the makespan using multiple reinforcement learning-based agents

机译：在线调度云工作流程的依赖任务，以增强资源利用率，并使用多种基于钢筋基于学习的代理来减少MakEspan

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Due to different heterogeneous cloud resources and diverse and complex applications of the users, an optimal task scheduling, which can satisfy users and cloud service providers with energy-saving and cost-effective use of resources, is a major issue in cloud computing. On the one hand, network users are demanding the quality assurance of their requested services, minimizing their costs, and their own data security, and on the other hand, the service providers consider less power consumption, more efficient use of resources, and optimal utilization. In dependent tasks dealing with massive data, resource scheduling is considered as an important challenge. Due to the time limitation of online scheduling process of dependent tasks, many existing methods of the literature are not able to guarantee the best resource utilization. In this paper, a reinforcement learning approach is exploited in a multi-agent system for task scheduling and resource provisioning, in order to reduce the makespan, minimize the required power, optimize the cost of using the resources, and maximize the utilization of the resources (considering their expiration time), simultaneously. The proposed algorithm has two phases. In the first phase, the tasks are scheduled using reinforcement learning techniques, and in the second one, considering the information obtained from the scheduling phase, resources are allocated in a multi-agent environment. The results of experiments show that this method improves the efficiency of the use of resources and reduces their costs. Moreover, the expiration time of the tasks is observed and the total execution time and energy consumption will be significantly reduced.
机译：由于不同的异构云资源和用户的多样化和复杂的应用，可以满足用户和云服务提供商以节能和高昂的资源使用资源，是云计算的主要问题。一方面，网络用户要求提供所要求的服务的质量保证，最大限度地减少其成本，以及他们自己的数据安全，另一方面，服务提供商考虑更少的功耗，更有效地利用资源，以及最佳利用。在处理大规模数据的依赖任务中，资源调度被视为一个重要的挑战。由于依赖任务的在线调度过程的时间限制，文献的许多现有方法无法保证最佳资源利用率。在本文中，在多助理系统中利用了加强学习方法，以进行任务调度和资源供应，以减少Mapspan，最大限度地减少所需的功率，优化使用资源的成本，并最大限度地提高资源的利用率（考虑到他们的到期时间）同时。所提出的算法有两个阶段。在第一阶段中，通过增强学习技术和在第二个中，考虑从调度阶段获得的信息，在多委托环境中分配资源。实验结果表明，该方法提高了资源使用的效率并降低了成本。此外，观察到任务的到期时间，并且将显着降低总执行时间和能量消耗。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号