A cloud resource management framework for multiple online scientific workflows using cooperative reinforcement learning agents

计算机科学 云计算 供应 调度(生产过程) 分布式计算 服务质量 工作流程 强化学习 能源消耗 资源管理(计算) 计算机网络 数据库 操作系统 经济 人工智能 生物 运营管理 生态学
作者
Ali Asghari,Mohammad Karim Sohrabi,Farzin Yaghmaee
出处
期刊:Computer Networks [Elsevier BV]
卷期号:179: 107340-107340 被引量:35
标识
DOI:10.1016/j.comnet.2020.107340
摘要

Cloud is a common distributed environment to share strong and available resources to increase the efficiency of complex and heavy calculations. In return for the cost paid by cloud users, a variety of services have been provided for them, the quality of which has been guaranteed and the reliability of their corresponding resources have been supplied by cloud service providers. Due to the heterogeneity of resources and their several shared applications, efficient scheduling can increase the productivity of cloud resources. This will reduce users’ costs and energy consumption, considering the quality of service provided for them. Cloud resource management can be conducted to obtain several objectives. Reducing user costs, reducing energy consumption, load balancing of resources, enhancing utilization of resources, and improving availability and security are some of the key objectives in this area. Several methods have been proposed for cloud resource management, most of which are focused on one or more aspects of these objectives of cloud computing. This paper introduces a new framework consisting of multiple cooperative agents, in which, all phases of the task scheduling and resource provisioning is considered and the quality of service provided to the user is controlled. The proposed integrated model provides all task scheduling and resource provisioning processes, and its various parts serve the management of user applications and more efficient use of cloud resources. This framework works well on dependent simultaneous tasks, which have a complicated process of scheduling because of the dependence of its sub-tasks. The results of the experiments show the better performance of the proposed model in comparison with other cloud resource management methods.
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