面向云环境数据中心的高效资源调度机制研究
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摘要
随着信息产业的快速发展以及互联网的广泛普及,数据中心作为信息服务提供平台发挥了重要的基石作用。IT的蓬勃发展使得网络和业务应用越来越多地向拥有大量服务器和存储集群的大型数据中心转移,因而数据中心的规模正经历着迅速地扩张。快速发展的数据中心正面临着诸多问题,其中急剧攀升的基础设施资源的需求与数据中心单一昂贵的资源提供方式之间的矛盾,使传统数据中心高成本、低效率、高能耗成为其中最严峻的挑战之一。因此,数据中心对提高资源利用率的迫切需求,促使人们寻求新的方式以建设下一代数据中心。
为实现资源的高效共享,以应对大数据高速增长势头而产生的云计算,凭借其更加灵活高效、低成本的运行方式成为下一代数据中心重要的发展趋势。面向云环境的数据中心,旨在基于大规模资源整合,实现高效、灵活地一致性计算与管理,以供应链方式提供共享的基础设施、信息与应用等IT服务。其中资源调度作为下一代数据中心部署大规模应用的重要保障,关系到数据中心的运营成本、整体性能以及可持续发展能力。特别是,当虚拟化成为云计算主要支撑技术后,资源虚拟化对数据中心的资源复用、关联、动态管理等方面都提出了新的挑战。这就需要研究者推动一系列创新技术去实现云计算的按需提供、弹性可扩展等特性。因此,本文重点关注面向云环境数据中心的高效资源调度。针对已有工作存在的不足,本文对基础设施资源调度技术展开了深入研究,主要包括以下三个方面：
(1)研究了基于多维协同聚合的虚拟机调度机制,以缓解云环境下由于数据中心虚拟化资源的一致共享和多租户应用异构性之间的矛盾,所带来的服务器内部多种资源间分配不均衡问题。本文提出了基于服务器聚合的多维协同调度方法,以提高面向云环境数据中心的基础设施资源利用率。我们首先将该问题映射为多维协同的可变向量装箱模型(MCVP),随后提出一种基于多维资源协同聚合的虚拟机调度算法(MCCA)。该算法利用分组遗传框架在群体编码等方面的特性来满足模型的群体优化属性需求,采用模糊逻辑方法设计了多维协同的搜索控制函数,并对核心算子进行基于概率选择和多属性决策的优化,以提高算法效率及求解质量。实验结果表明该算法对均衡多维资源,从而提高资源综合利用率具有一定优势。
(2)研究了通信关联感知的多层应用映射策略,以解决由数据中心网络资源不合理使用所导致的网络压力增大问题。数据中心网络资源有限且具有一定的层次关系,其调度方法关系到数据中心的整体性能和可扩展性等。数据中心网络资源消耗与两类通信关系相关,即虚拟机之间的通信模式和服务器间的拓扑形态。本文提出合理关联两类通信关系的映射策略优化思想,通过将较大通信流量映射在低复用链路上,来提高网络资源的效用,降低网络负载,从而有助于扩展数据中心的承载能力。我们将该问题映射为扩展的二次分配问题(Quadratic Assignment Problem, QAP),并提出一种考虑多层应用可用性映射冲突的双阶段优化算法(DOPA),在合理的时间内获得满足模型目标的近似最优解。实验结果表明该算法实现了优化数据中心网络资源效用,降低网络压力以提高可扩展性的目标。
(3)研究了面向并发序列的虚拟机迁移机制,以满足大规模资源共享的灵活性和时序高效需求。云环境数据中心的资源高聚合度,使大量虚拟机的并发迁移操作产生迁移序列约束,同时热迁移的延迟对大规模调度带来了更多挑战。本文采用基于流(flow-based)模型对并发序列的虚拟机迁移场景进行抽象,给出了VMM层面对整体调度延迟的评估方法。通过降低dirty page生成速率和动态调节迁移带宽,我们提出了基于" min-max "原则的面向并发序列虚拟机自适应迁移调度算法(SAAM)。仿真结果表明,SAAM算法不仅能够降低单一虚拟机迁移操作的延迟,而且能够根据flow-based模型的技术特点和并发序列的约束,优化并发迁移场景的整体调度延迟开销,论证了该算法在单一虚拟机和并发虚拟机迁移序列场景下,对热迁移延迟指标的优化作用。
With the rapid development of information industry and the widely spreading of Internet technology, data center plays an important role in supporting the informational service. More and more companies transfer their applications to large scaled data center which hosting numerous computing resources. Thus, the scale of the data center is undergoing a rapid expansion. The booming trend also leads a lot of problems, in which the resources scheduling is one of the most crucial issues to foundation of data centers. The contradiction between inefficient costly resource provision mechanism and the sharply rised resource demand challenges the sustainable growth of data centers. Therefore, it is necessary to seek new ways of building next-generation data center under the urgent need of improving the infrastructure utilization.
Cloud computing tends to leverage its newly resource provision pattern helps data center overcoming this weakness. The goal of massively scalable cloud data centers is to make applications reside in where computational resources can be dynamically provisioned and shared to achieve significant economies of scale. How to design the efficient resource scheduling mechanism of cloud data center is a considerable problem. Faced the new challenge of resource sharing and dynamic management involved by virtualization, new researches will be raised to meet the need of on-demand, elastic and scalability in cloud data centers. Therefore, this dissertation focuses on the key techniques of improving the efficiency of cloud data center resource scheduling. Based on analysis of current techniques, we make an in-depth researching on the potential methods on the enhancement of resource utility. The main contributions of our work include:
(1) We proposed a multidimensional coordination based virtual machine (VM) scheduling mechanism. It aims at improving the resource utilization of data center by balanced usage of multiple resources, as multi-tenant technology in cloud computing allows heterogeneous virtual machine share a virtualized data center. The problem is described as a vector packing model with multi-dimensional coordination. We carried out a group genetic based multi-dimensional coordination scheduling algorithm, which leverage the advantage of group characteristic. To guide the solution searching, a fuzzy logic based multidiemsional fitness function is raised. As well, innovative optimization of key operators is put forward to improve the solution quality. The experiment results proved that our algorithm would efficiently reduce the imbalance in multiple resources and finally increased the utilization of the infrastructure resources in cloud data centers.
(2) In order to efficient use of the network resource in cloud data centers, we proposed a communication-aware multi-tier application placement problem with conflict-avoidance. The network resource in data centers is often hierarchical organized and limited, which directly affects the entire performance and the scalability of data centers. The cost of network resource is closely related to two parameters namely traffic patterns among VMs and the topology of the data center networks. For the purpose of increasing scalability of data center networks, we prefer mapping much heavier traffic onto low-cost links to relax the communication load. The C2VMPP is modeled as a variant of Quadratic Assignment Problem, and a dual-stage optimization placement algorithm with the consideration of multi-tier application characteristics is introduced to obtain an approximate solution of C2VMPP. The evaluation results of DOPA verified our motivation for supporting higher scalability by optimizing the network utilization and balancing the total communication load.
(3) In order to providing both the flexibility and time-efficiency of large-scale resource sharing in data centers, we focus on the scheduling mechanism of the parallel sequence oriented VM migration problem. The high consolidation level of infrastructure in cloud data centers results in more sequence constraints when the hypervisor tries to migrate a great number of VMs at the same time. Moreover, the delay caused by live migration exacerbates the challenge of large-scale VMs parallel scheduling. Throughout this paper, we first modeled the scenario of parallel sequence oriented VM migration problem as a flow-based graph, and then described an effective method for total migration delay evaluating from the perspective of VMM. Based on the principal of "min-max", we proposed a Scheduling Algorithm of Adaptive Migration (SAAM) to solve the parallel sequence oriented VM migration problem, which works by reducing the generating speed of dirty pages and/or adjusting bandwidth for migration activity. The simulation results indicate that not only our SAAM algorithm would decrease the migration delay for a single VM migration operation, but also contribute to reducing the total migration delay of parallel VM migration with sequence constraints according to the flow-based modeling. As a conclusion, SAAM algorithm is helpful to optimize the delay of live migration for both single and parallel VM migration scenarios.
为实现资源的高效共享,以应对大数据高速增长势头而产生的云计算,凭借其更加灵活高效、低成本的运行方式成为下一代数据中心重要的发展趋势。面向云环境的数据中心,旨在基于大规模资源整合,实现高效、灵活地一致性计算与管理,以供应链方式提供共享的基础设施、信息与应用等IT服务。其中资源调度作为下一代数据中心部署大规模应用的重要保障,关系到数据中心的运营成本、整体性能以及可持续发展能力。特别是,当虚拟化成为云计算主要支撑技术后,资源虚拟化对数据中心的资源复用、关联、动态管理等方面都提出了新的挑战。这就需要研究者推动一系列创新技术去实现云计算的按需提供、弹性可扩展等特性。因此,本文重点关注面向云环境数据中心的高效资源调度。针对已有工作存在的不足,本文对基础设施资源调度技术展开了深入研究,主要包括以下三个方面：
(1)研究了基于多维协同聚合的虚拟机调度机制,以缓解云环境下由于数据中心虚拟化资源的一致共享和多租户应用异构性之间的矛盾,所带来的服务器内部多种资源间分配不均衡问题。本文提出了基于服务器聚合的多维协同调度方法,以提高面向云环境数据中心的基础设施资源利用率。我们首先将该问题映射为多维协同的可变向量装箱模型(MCVP),随后提出一种基于多维资源协同聚合的虚拟机调度算法(MCCA)。该算法利用分组遗传框架在群体编码等方面的特性来满足模型的群体优化属性需求,采用模糊逻辑方法设计了多维协同的搜索控制函数,并对核心算子进行基于概率选择和多属性决策的优化,以提高算法效率及求解质量。实验结果表明该算法对均衡多维资源,从而提高资源综合利用率具有一定优势。
(2)研究了通信关联感知的多层应用映射策略,以解决由数据中心网络资源不合理使用所导致的网络压力增大问题。数据中心网络资源有限且具有一定的层次关系,其调度方法关系到数据中心的整体性能和可扩展性等。数据中心网络资源消耗与两类通信关系相关,即虚拟机之间的通信模式和服务器间的拓扑形态。本文提出合理关联两类通信关系的映射策略优化思想,通过将较大通信流量映射在低复用链路上,来提高网络资源的效用,降低网络负载,从而有助于扩展数据中心的承载能力。我们将该问题映射为扩展的二次分配问题(Quadratic Assignment Problem, QAP),并提出一种考虑多层应用可用性映射冲突的双阶段优化算法(DOPA),在合理的时间内获得满足模型目标的近似最优解。实验结果表明该算法实现了优化数据中心网络资源效用,降低网络压力以提高可扩展性的目标。
(3)研究了面向并发序列的虚拟机迁移机制,以满足大规模资源共享的灵活性和时序高效需求。云环境数据中心的资源高聚合度,使大量虚拟机的并发迁移操作产生迁移序列约束,同时热迁移的延迟对大规模调度带来了更多挑战。本文采用基于流(flow-based)模型对并发序列的虚拟机迁移场景进行抽象,给出了VMM层面对整体调度延迟的评估方法。通过降低dirty page生成速率和动态调节迁移带宽,我们提出了基于" min-max "原则的面向并发序列虚拟机自适应迁移调度算法(SAAM)。仿真结果表明,SAAM算法不仅能够降低单一虚拟机迁移操作的延迟,而且能够根据flow-based模型的技术特点和并发序列的约束,优化并发迁移场景的整体调度延迟开销,论证了该算法在单一虚拟机和并发虚拟机迁移序列场景下,对热迁移延迟指标的优化作用。
With the rapid development of information industry and the widely spreading of Internet technology, data center plays an important role in supporting the informational service. More and more companies transfer their applications to large scaled data center which hosting numerous computing resources. Thus, the scale of the data center is undergoing a rapid expansion. The booming trend also leads a lot of problems, in which the resources scheduling is one of the most crucial issues to foundation of data centers. The contradiction between inefficient costly resource provision mechanism and the sharply rised resource demand challenges the sustainable growth of data centers. Therefore, it is necessary to seek new ways of building next-generation data center under the urgent need of improving the infrastructure utilization.
Cloud computing tends to leverage its newly resource provision pattern helps data center overcoming this weakness. The goal of massively scalable cloud data centers is to make applications reside in where computational resources can be dynamically provisioned and shared to achieve significant economies of scale. How to design the efficient resource scheduling mechanism of cloud data center is a considerable problem. Faced the new challenge of resource sharing and dynamic management involved by virtualization, new researches will be raised to meet the need of on-demand, elastic and scalability in cloud data centers. Therefore, this dissertation focuses on the key techniques of improving the efficiency of cloud data center resource scheduling. Based on analysis of current techniques, we make an in-depth researching on the potential methods on the enhancement of resource utility. The main contributions of our work include:
(1) We proposed a multidimensional coordination based virtual machine (VM) scheduling mechanism. It aims at improving the resource utilization of data center by balanced usage of multiple resources, as multi-tenant technology in cloud computing allows heterogeneous virtual machine share a virtualized data center. The problem is described as a vector packing model with multi-dimensional coordination. We carried out a group genetic based multi-dimensional coordination scheduling algorithm, which leverage the advantage of group characteristic. To guide the solution searching, a fuzzy logic based multidiemsional fitness function is raised. As well, innovative optimization of key operators is put forward to improve the solution quality. The experiment results proved that our algorithm would efficiently reduce the imbalance in multiple resources and finally increased the utilization of the infrastructure resources in cloud data centers.
(2) In order to efficient use of the network resource in cloud data centers, we proposed a communication-aware multi-tier application placement problem with conflict-avoidance. The network resource in data centers is often hierarchical organized and limited, which directly affects the entire performance and the scalability of data centers. The cost of network resource is closely related to two parameters namely traffic patterns among VMs and the topology of the data center networks. For the purpose of increasing scalability of data center networks, we prefer mapping much heavier traffic onto low-cost links to relax the communication load. The C2VMPP is modeled as a variant of Quadratic Assignment Problem, and a dual-stage optimization placement algorithm with the consideration of multi-tier application characteristics is introduced to obtain an approximate solution of C2VMPP. The evaluation results of DOPA verified our motivation for supporting higher scalability by optimizing the network utilization and balancing the total communication load.
(3) In order to providing both the flexibility and time-efficiency of large-scale resource sharing in data centers, we focus on the scheduling mechanism of the parallel sequence oriented VM migration problem. The high consolidation level of infrastructure in cloud data centers results in more sequence constraints when the hypervisor tries to migrate a great number of VMs at the same time. Moreover, the delay caused by live migration exacerbates the challenge of large-scale VMs parallel scheduling. Throughout this paper, we first modeled the scenario of parallel sequence oriented VM migration problem as a flow-based graph, and then described an effective method for total migration delay evaluating from the perspective of VMM. Based on the principal of "min-max", we proposed a Scheduling Algorithm of Adaptive Migration (SAAM) to solve the parallel sequence oriented VM migration problem, which works by reducing the generating speed of dirty pages and/or adjusting bandwidth for migration activity. The simulation results indicate that not only our SAAM algorithm would decrease the migration delay for a single VM migration operation, but also contribute to reducing the total migration delay of parallel VM migration with sequence constraints according to the flow-based modeling. As a conclusion, SAAM algorithm is helpful to optimize the delay of live migration for both single and parallel VM migration scenarios.
引文
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