私有云中虚拟资源的节能调度研究
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摘要
云计算(Cloud computing)是一种新的基于虚拟资源池的大规模分布式计算模式,是分布式计算、并行计算、网格计算、效用计算、Web service等的不断融合与深入发展。云计算使用户无论身在何处,只需要接通网络,就可以通过即付即用、按用量收费的方式来获取云数据中心提供的各种IT相关服务,如基础设施、平台与软件等。同时它也符合绿色计算的基本思想,通过弹性高可扩展地管理资源池,同时降低客户端的硬件需求(“瘦”终端),它能够较大程度地缩减能源消耗,降低用户与数据中心的成本,是低碳经济时代最有前景的计算模式之一。
目前,作为云计算应用模式之一的私有云正受到国内外越来越多企业与研究机构的青睐。与公有云相比,私有云具有其自身的一些特点与需求,尤其是在资源管理与节能调度方面。本文通过深度挖掘这些特性,提出了一系列节能调度方法,能够在基本保证任务执行效率与系统吞吐率的前提下,尽可能地减少能耗,具体有以下几个方面,其中(2)、(3)和(4)为本文的创新点。
(1)通过深入考察私有云的产生原因与应用场景,结合虚拟化环境下资源管理与节能调度的基本问题,总结出了私有云在资源管理方面的一系列敏感特性。基于这些特性,采用三个基本指标作为衡量节能调度优劣的依据,即请求响应时间、节能量与负载均衡程度,并通过四个范式来规范节能与负载均衡之间的矛盾。
(2)在虚拟机节能调度方面,有别于当前大多数研究中所采用的通过阈值来改变目标节点状态的方式,提出了一种基于布局的方法,它采用主动休眠机制,并能够通过预调度来提高任务的响应效率,通过最小负载优先法来均衡负载。实验表明,这种方法能够降低虚拟机请求的响应时间,同时减少能源消耗,还能够使负载更均衡地分布。
(3)在虚拟磁盘节能调度方面,提出了一种节能优化方法,其原型是虚拟机调度中基于布局的方法。该方法能够根据用户请求的规模动态改变虚拟磁盘工作池的规模,并缓解因磁盘休眠而造成的响应时间延长问题。实验表明,这种方法能够有效缩短用户的等待时间,并减少磁盘空转时间。
(4)在虚拟网络节能调度方面,对于因多虚拟机通信造成的内部网络拥塞问题,提出了一种基于对称多处理虚拟机的调度方法。这种方法能够有效地减少安全组内虚拟机的个数,降低了通信门槛,从而提高了并行计算任务的执行效率。同时,它通过减少通信域的数量,大大降低了对内部网络的通信依赖,从而达到了节能网络的目标。
(5)通过对已有计算机支持的协作学习系统的综述,发现了底层资源可扩展性不强、能耗较高等问题。通过将私有云作为其底层架构,并应用上述节能机制,实现了基于弹性云服务的节能协作学习系统。该系统能够为用户提供在线虚拟化的协作学习环境,为资源的弹性扩展提供良好支持,并能够有效地利用能源。
As a novel large-scale distributed computing paradigm based on virtualized resource pool, cloud computing derived from the ever-increasing interaction and profound development of distributed computing, parallel computing, grid computing, utility computing, web services, etc. If only with accesses to cloud data centers, cloud computing could enable users all over the planet to on-demand leverage a variety of IT-related services based on a pay-per-use model, e.g. infrastructure, platform, software, etc. In addition, cloud computing also corresponds with the basic idea of green computing. Through elastic and high-scalable management of the resource pool, as well as relaxation of the terminal (the“thin”terminal), cloud computing can conserve much energy and cut the cost of both user’s and data center’s down. It is one of the most promising computing paradigms in the coming low carbon economy.
Recently, one of the applied models of cloud computing in industry, private cloud, is getting more and more popular among lots of companies and research centers around the world. Compared to public cloud, private cloud has some special features and requirements, especially respect to resource management and power-aware scheduling. After in-depth exploring theses features, we propose couples of power-aware approaches, which could conserve more energy with acceptable overhead on job efficiency and data center’s throughput rate, including the following aspects, among which (2), (3) and (4) are contributions of this dissertation.
(1) Under in-depth exploring the cause and the applied scenarios of private clouds, in association with the basic problem of resource management and power-aware scheduling in virtualized environments, we sum up series of sensitive characteristics of private clouds. In this manner, three metrics are applied, i.e. response time of requests, conserved energy and load balancing level, to evaluate the performance of power-aware scheduling solutions. Additionally, we also introduce four paradigms to manage the tradeoff problem between energy consumption and load balancing.
(2) As respect to power-aware scheduling of virtual machines, a layout-based approach is proposed using active sleep mechanism, other than addressing the modes of target nodes via thresholds according to most previous researches. Through this approach, the response time can be cut down by a pre-power technique, and the workloads across nodes can be balanced by a min-load-first algorithm. And the experiments show that, this approach can not only shorten the response time of requests, conserve more energy, but also achieve higher level of load balancing.
(3) As respect to power-aware scheduling of virtual disks, a power-aware optimization approach is proposed, which is derived from the layout-based approach proposed in power-aware scheduling of virtual machines. It can dynamically adjust the volume of the working pool and alleviate the prolonged problem of response time in case target disk is being stuck on sleep mode. And the experiments show that, this approach can effectively cut job’s response time down, meanwhile, reduce the idle time of physical disks.
(4) As respect to power-aware scheduling of virtual network, a symmetric multi-processing virtual machine based approach is introduced against the traffic jam problem due to communications among multiple virtual machines within local network. This approach can effectively reduce the number of virtual machines within a secure group, lower the communication threshold and promote the performance of parallel computing jobs. Moreover, through cutting down the number of communication zones, this approach can greatly relax the coupling connection between virtual group and local network, and therefore, it can meet the objective of power-aware network.
(5) After reviewing existing computer-supported collaborative learning systems, some limitations related to underlying resources, such as insufficient scalability and high energy dissipation, are concluded. Using private clouds as the base infrastructure, an elastic cloud service based power-aware collaborative learning system is proposed using the above-mentioned power-aware approaches. It can enable users with virtualized collaborative environment on demand, support elastic scalability of resources and make good use of energy.
目前,作为云计算应用模式之一的私有云正受到国内外越来越多企业与研究机构的青睐。与公有云相比,私有云具有其自身的一些特点与需求,尤其是在资源管理与节能调度方面。本文通过深度挖掘这些特性,提出了一系列节能调度方法,能够在基本保证任务执行效率与系统吞吐率的前提下,尽可能地减少能耗,具体有以下几个方面,其中(2)、(3)和(4)为本文的创新点。
(1)通过深入考察私有云的产生原因与应用场景,结合虚拟化环境下资源管理与节能调度的基本问题,总结出了私有云在资源管理方面的一系列敏感特性。基于这些特性,采用三个基本指标作为衡量节能调度优劣的依据,即请求响应时间、节能量与负载均衡程度,并通过四个范式来规范节能与负载均衡之间的矛盾。
(2)在虚拟机节能调度方面,有别于当前大多数研究中所采用的通过阈值来改变目标节点状态的方式,提出了一种基于布局的方法,它采用主动休眠机制,并能够通过预调度来提高任务的响应效率,通过最小负载优先法来均衡负载。实验表明,这种方法能够降低虚拟机请求的响应时间,同时减少能源消耗,还能够使负载更均衡地分布。
(3)在虚拟磁盘节能调度方面,提出了一种节能优化方法,其原型是虚拟机调度中基于布局的方法。该方法能够根据用户请求的规模动态改变虚拟磁盘工作池的规模,并缓解因磁盘休眠而造成的响应时间延长问题。实验表明,这种方法能够有效缩短用户的等待时间,并减少磁盘空转时间。
(4)在虚拟网络节能调度方面,对于因多虚拟机通信造成的内部网络拥塞问题,提出了一种基于对称多处理虚拟机的调度方法。这种方法能够有效地减少安全组内虚拟机的个数,降低了通信门槛,从而提高了并行计算任务的执行效率。同时,它通过减少通信域的数量,大大降低了对内部网络的通信依赖,从而达到了节能网络的目标。
(5)通过对已有计算机支持的协作学习系统的综述,发现了底层资源可扩展性不强、能耗较高等问题。通过将私有云作为其底层架构,并应用上述节能机制,实现了基于弹性云服务的节能协作学习系统。该系统能够为用户提供在线虚拟化的协作学习环境,为资源的弹性扩展提供良好支持,并能够有效地利用能源。
As a novel large-scale distributed computing paradigm based on virtualized resource pool, cloud computing derived from the ever-increasing interaction and profound development of distributed computing, parallel computing, grid computing, utility computing, web services, etc. If only with accesses to cloud data centers, cloud computing could enable users all over the planet to on-demand leverage a variety of IT-related services based on a pay-per-use model, e.g. infrastructure, platform, software, etc. In addition, cloud computing also corresponds with the basic idea of green computing. Through elastic and high-scalable management of the resource pool, as well as relaxation of the terminal (the“thin”terminal), cloud computing can conserve much energy and cut the cost of both user’s and data center’s down. It is one of the most promising computing paradigms in the coming low carbon economy.
Recently, one of the applied models of cloud computing in industry, private cloud, is getting more and more popular among lots of companies and research centers around the world. Compared to public cloud, private cloud has some special features and requirements, especially respect to resource management and power-aware scheduling. After in-depth exploring theses features, we propose couples of power-aware approaches, which could conserve more energy with acceptable overhead on job efficiency and data center’s throughput rate, including the following aspects, among which (2), (3) and (4) are contributions of this dissertation.
(1) Under in-depth exploring the cause and the applied scenarios of private clouds, in association with the basic problem of resource management and power-aware scheduling in virtualized environments, we sum up series of sensitive characteristics of private clouds. In this manner, three metrics are applied, i.e. response time of requests, conserved energy and load balancing level, to evaluate the performance of power-aware scheduling solutions. Additionally, we also introduce four paradigms to manage the tradeoff problem between energy consumption and load balancing.
(2) As respect to power-aware scheduling of virtual machines, a layout-based approach is proposed using active sleep mechanism, other than addressing the modes of target nodes via thresholds according to most previous researches. Through this approach, the response time can be cut down by a pre-power technique, and the workloads across nodes can be balanced by a min-load-first algorithm. And the experiments show that, this approach can not only shorten the response time of requests, conserve more energy, but also achieve higher level of load balancing.
(3) As respect to power-aware scheduling of virtual disks, a power-aware optimization approach is proposed, which is derived from the layout-based approach proposed in power-aware scheduling of virtual machines. It can dynamically adjust the volume of the working pool and alleviate the prolonged problem of response time in case target disk is being stuck on sleep mode. And the experiments show that, this approach can effectively cut job’s response time down, meanwhile, reduce the idle time of physical disks.
(4) As respect to power-aware scheduling of virtual network, a symmetric multi-processing virtual machine based approach is introduced against the traffic jam problem due to communications among multiple virtual machines within local network. This approach can effectively reduce the number of virtual machines within a secure group, lower the communication threshold and promote the performance of parallel computing jobs. Moreover, through cutting down the number of communication zones, this approach can greatly relax the coupling connection between virtual group and local network, and therefore, it can meet the objective of power-aware network.
(5) After reviewing existing computer-supported collaborative learning systems, some limitations related to underlying resources, such as insufficient scalability and high energy dissipation, are concluded. Using private clouds as the base infrastructure, an elastic cloud service based power-aware collaborative learning system is proposed using the above-mentioned power-aware approaches. It can enable users with virtualized collaborative environment on demand, support elastic scalability of resources and make good use of energy.
引文
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