云环境下基于预测的资源调度研究

发布时间：2018-07-29 20:03

【摘要】：近几年,云计算技术越来越成熟,并被成功运用到了商业、教育、科研等领域,成为炙手可热的计算机技术研究热点。相较之前的Web服务,云计算具有更高的可靠性、扩展性和灵活性,它利用虚拟机、虚拟内存等技术实现了虚拟化,形成了按需支付的模式,为服务提供商和终端用户提供了诸多方便。在此基础上,由计算机资源封装而成的服务数量不断增加并被发布到云计算平台上为终端用户提供服务。随着网络上可用服务数量的增加,用户不再仅仅关注服是否可用,而是更加关注服务的质量(Quality of Service,QoS),比如,执行时间、花费多少等。面对任务的繁多和用户服务高质量的需求,如何对云环境下的资源和任务进行合理调度成为云计算研究的主要问题之一。另一方面,云计算系统中数据中心服务器数量多、服务资源异构多样、用户基数大、用户服务约束条件各不相同、应用任务类型各异,云计算数据中必须能够时刻可靠地处理海量用户任务和数据,如何及时高效并安全的将结果反馈给终端用户并能满足终端用户的需求成为服务提供商面临的最大挑战。同时,完成任务调度所产生的成本也是服务提供商最关心的问题之一。因此,高效的调度算法成为云环境下研究的重难点。在此基础上,本文提出了一种改进的蚁群算法,该算法综合了计算资源(通常指虚拟机Virtual Machines,VMs)的可获得性以及具有不同服务质量(QoS)约束的任务的特性。鉴于传统蚁群调度算法一般只考虑计算资源的特性,而忽略了用户约束条件以及云资源的异构性,本文中的算法将用户任务分为计算密集型和网络交互密集型两种类型,并根据QoS优先级和虚拟机处理速度分别对用户任务和虚拟机进行排序,旨在能够在异构环境中具有不同资源参数的计算资源上对具有不同服务质量需求的任务进行合理调度,以节约执行时间和成本,同时满足服务提供商和终端用户的需求。实验结果表明,本文中提出的基于预测的调度算法更能倾向于找到最合理的任务虚拟机分配对,并且反复执行该算法的情况下能在一定程度上减少任务总执行时间和成本。
[Abstract]:In recent years, cloud computing technology has become more and more mature, and has been successfully applied to business, education, scientific research and other fields, and has become a hot research hotspot of computer technology. Compared with the previous Web services, cloud computing has higher reliability, scalability and flexibility, it uses virtual machine, virtual memory and other technologies to achieve virtualization, forming an on-demand payment model, It provides a lot of convenience for service providers and end users. On this basis, the number of services encapsulated by computer resources continues to increase and is released to the cloud computing platform to provide services to end users. With the increase of the number of available services on the network, users are not only concerned about the availability of service, but also more about the quality of service, such as the execution time, the amount of time spent, and so on. In the face of various tasks and high quality user service, how to reasonably schedule resources and tasks in cloud environment has become one of the main problems in cloud computing research. On the other hand, the number of data center servers in cloud computing systems is large, the service resources are heterogeneous, the user base is large, the user service constraints are different, and the types of application tasks are different. Cloud computing data must be able to deal with massive user tasks and data reliably at all times. How to efficiently and safely feedback the results to end users and meet the needs of end users becomes the biggest challenge for service providers. At the same time, the cost of task scheduling is also one of the most concerned issues for service providers. Therefore, efficient scheduling algorithm has become a heavy and difficult problem in cloud environment. On this basis, an improved ant colony algorithm is proposed, which combines the availability of computing resources (usually referred to as virtual machine Virtual machines) and the properties of tasks with different quality of service (QoS) constraints. Since the traditional ant colony scheduling algorithm only considers the characteristics of computing resources, but ignores the user constraints and the heterogeneous nature of cloud resources, the algorithm in this paper divides user tasks into two types: computational intensive and network interaction intensive. According to the QoS priority and the processing speed of the virtual machine, the user tasks and the virtual machines are sorted respectively. The purpose of this paper is to schedule reasonably the tasks with different QoS requirements on the computing resources with different resource parameters in the heterogeneous environment. To save execution time and cost, while meeting the needs of service providers and end users. The experimental results show that the proposed scheduling algorithm based on prediction is more inclined to find the most reasonable task virtual machine allocation pairs and can reduce the total task execution time and cost to a certain extent when the algorithm is executed repeatedly.
【学位授予单位】：华北电力大学(北京)
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP3;TP18

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本文编号：2153842