面向服务系统的自适应资源管理技术研究
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摘要
随着服务计算的日趋成熟以及移动互联网、物联网和软件即服务等新型服务模式的涌现,服务系统不断演化,呈现出多租户、结构复杂、高并发访问以及多终端接入的特性。与此同时,大部分服务系统采用独占资源的部署方式,服务系统规模的扩容依赖于基础设施的不断投入,因而降低了服务系统的资源利用率,提高了服务系统的运营成本。服务系统越来越需要自动化的方式来整合各类资源,同时需要具备可伸缩的规模化调度的能力,以提高IT资源的总体利用率,满足各类服务组件的服务质量要求,提升服务交付效率。
云计算是IT服务提供方式的重要转变,通过虚拟化技术将服务和数据储存在可扩展的共享资源池中,并能根据用户需求快速伸缩提供,为服务系统设计、开发和部署提供了全新的交付方案。本文以虚拟化技术为支撑技术,研究面向服务系统的自适应资源管理技术,依据自主计算的思想,提出自适应资源管理的体系架构,并从资源识别、资源部署、资源提供以及资源协商四个方面展开研究。通过构建自组织资源协同覆盖网,设计高效虚拟机部署机制以及动态资源提供策略,建立适应性资源协商与竞价模型,实现自动化的虚拟化资源管理,保证服务交付的服务质量要求,并且提高整体的资源使用率。
本文主要的创新点为：
1)针对资源池中集中式管理存在的性能瓶颈和单点失效问题,提出一种基于Gossip协议的混合多属性覆盖网(Gossip-based Hybrid Multi-attribute Overlay, GHMO)拓扑结构,以对等的方式构建了自组织的资源管理基础设施。混合多属性覆盖网同时维护了结构化拓扑和非结构化拓扑,采用混合路由的模式实现了多属性精确查询和多属性范围查询,提高了资源查询的效率。在此基础上,考虑网络时延对资源查询的影响,构建了权值覆盖网的模型,并设计了最近邻居选择策略,通过筛选网络距离较近的邻居,优化了路由的路径选择。仿真结果表明,相比于多属性混合拓扑MAHO, GHMO的资源查询效率提高了33%,同时通过引入最近邻居选择策略,GHMO的路由开销减少了14.2%。
2)针对资源池中多种类型资源分配不均而导致的服务器蔓延现象和能耗偏高问题,提出异构资源池环境下一种能耗感知的启发式向量部署机制PHVP,充分考虑物理机各维度的容量和虚拟机的资源需求特性,通过多维资源协同实现虚拟机的有效放置,以达到服务器整合以及降低能耗的目的。PHVP部署机制采用元启发式算法GRASP改善启发式算法存在的局部优化问题,实现虚拟机的高效放置,并采用能耗感知的主机开启算法优化异构资源池中的物理机使用。仿真结果表明,在异构资源池环境下,相比于传统向量装箱算法和启发式算法,PHVP算法节省了10%的物理机开启数量以及10.3%的总能耗。
3)针对以截止时间为约束的高并发计算任务的服务质量要求,提出了一种面向截止时间约束的动态资源提供策略,通过动态调整资源池中虚拟机的数量以及任务队列中的任务执行顺序,实现动态资源提供以适应不断变化的请求负载规模,以满足各种优先级别任务调度要求。该资源提供策略基于排队论建立面向计算任务的弹性资源供给模型ERPM,周期性根据任务队列的信息调整资源池虚拟机数量,根据WFQBS算法计算每个优先级别的权值,同时结合SLA违例开销调度任务队列中的任务出队顺序。仿真实验结果表明,ERPM具有良好的弹性资源提供能力,并且相比于FCFS、SJF和EDF调度算法,WFQBS调度算法能公平调度不同优先级别的任务,减少了SLA违例开销。
4)针对服务资源市场中资源提供者和资源消费者异构的资源供需状态以及资源交易目标,提出了基于组合／多数量双向拍卖的资源竞价模型BMDA,以买卖双方竞价形式来决定资源分配方案,充分激励资源市场参与者的积极性,以最大化社会效用。在此基础上,提出了两种拍卖机制,基于线性规划的拍卖机制BMDA-LP和基于贪心算法的拍卖机制BMDA-GREEDY,并分析了两种拍卖机制的策略性防伪、预算平衡和个人理性等三种特性。仿真实验结果表明,这两种拍卖机制都能根据资源供需情况进行市场调节功能,并且BMDA-LP的拍卖效率好于BMDA-GREEDY,社会化效用、用户满意度和资源使用率分别提高了161%、70%和37%。
With the matures of service computing and the emergence of new service paradigms, such as mobile internet, Internet of Things and Software as a Service, service systems have evolved to multi-tenant complicated system with high concurrent and multiple device access. Meanwhile, most service systems have been deployed on exclusive infrastructure and the expansion of service systems rely on the unceasing infrastructure investment, which decreases resource utilization and increases the operation cost of service systems. Service systems require more automatic mechanism to consolidate various resources and more elastic strategy to schedule resource, in order to increase total resource utilization, meet the QoS requirement of various service components and improve the efficiency of service delivery
Cloud Computing is the significant transformation of IT service provisioning paradigm. In this paradigm, service and data are stored in scalable shared resource pool via virtualization technology, and can be elastically provisioned to adapt to users' demand, which introduced novel delivery solution for the design, development and deploy of service systems. Based on virtualization technology and the concept of autonomic computing, this paper studies adaptive resource management for service systems. We propose the architecture of adaptive resource management and study four related issues, that is, resource discovery, resource deployment, resource provision and resource negotiation. We construct self-organization collaborated resource overlay, design efficient virtual machine placement mechanism and dynamic resource provisioning strategy, and build up adaptive resource negotiation and bidding model, to achieve automatic virtual resource management. The proposed resource management architecture would guarantee the quality of service delivery and improve total resource utilization.
The major contribution of this thesis is as following:
1) According to bottleneck of performance and point of failure introduced by centralized management of resource pool, we propose a Gossip-based Hybrid Multi-attribute Overlay (GHMO) to construct self-organization resource infrastructure with peer-to-peer fashion. GHMO maintain both of structured and unstructured topology, and use hybrid routing algorithm to implement multi-attribute accurate query and range query, which would raise the efficiency of resource query. Meanwhile, a weight overlay is introduced in the case of network latency, and a nearest neighbor selection strategy is raised to optimize the choice of query routing by selecting neighbor nearby to transmit query. Simulation results show that, compared with Multi-Attribute Hybrid Overlay (MAHO), query efficiency of GHMO is increased by33%. Also, the routing cost is reduced by14.2%with nearest neighbor selection strategy.
2) To deal with server sprawl and high energy consumption caused by unbalanced allocation of multiple resources in resource pool, we propose Power-aware Heuristic Vector Placement (PHVP) mechanism in heterogeneous cloud scenarios. The proposed mechanism considers multiple dimensions of the capacity of physical machines and the demand of virtual machines (VM), and utilizes multiple dimension collaboration to place virtual machines, in order to achieve server consolidation and reduce energy consumption. PHVP apply Greedy Randomized Adaptive Search Procedures (GRASP) to reduce of local optima introduced by classical heuristic methods and improve the efficiency of VM placement. Also, power-aware best-fit host activation algorithm is introduced to optimize the usage of physical machines in heterogeneous resource pool. Simulation results show that, in heterogeneous resource pool, PHVP outperforms FFD and vector based approach, decreasing amount of physical machines and total power consumption by10%and10.3%respectively.
3) To satisfy the demand of high concurrent and deadline constraint computing tasks, we propose a dynamic resource provisioning strategy for deadline constraint tasks. The proposed strategy adjusts the amount of virtual machines in resource pool and the order of task execution dynamically, to adapt to varying request workload and fulfill different level of QoS. We design analytical provision model Elastic Resource Provisioning Model (ERPM) for adaptive provision based on queuing theory, by adjusting the amount of virtual machines in resource pool periodically based on the status of task queue. Also, we schedule the order of task execution based on the weight of each priority calculated by Weighted Fair Queuing (WFQ) algorithm and SLA violation cost. Simulation results show that, ERPM give elastic resource provisioning for dynamic workload. Also, compared with FCFS, SJF and EDF, WFQBS algorithm could fairly schedule tasks with different priority, and reduce the total cost of SLA violation.
4) To cope with various demand/supply amounts and different trade targets of resource provider and resource consumer in service resource market, we propose resource bidding model based on Bundle Multiunit Double Auction (BMDA). The bidding model would determine resource allocation scheme by bidding among buyer and seller, to inspire trade enthusiasm of market participants and maximum social utility. Based on this model, we propose two auction mechanism, linear programming based mechanism BMDA-LP and greedy algorithm based mechanism BMDA-GREEDY. These two auction mechanisms are proved to be strategy-proof, budget-balanced and individual rational. Simulation results show that, two auction mechanisms could regulate auction market adapted to resource demand/supply. Moreover, the auction efficiency of BMDA-LP outperforms that of BMDA-GREEDY, with the growth of161%,70%and37%in social utility, user satisfaction degree and resource utilization respectively
云计算是IT服务提供方式的重要转变,通过虚拟化技术将服务和数据储存在可扩展的共享资源池中,并能根据用户需求快速伸缩提供,为服务系统设计、开发和部署提供了全新的交付方案。本文以虚拟化技术为支撑技术,研究面向服务系统的自适应资源管理技术,依据自主计算的思想,提出自适应资源管理的体系架构,并从资源识别、资源部署、资源提供以及资源协商四个方面展开研究。通过构建自组织资源协同覆盖网,设计高效虚拟机部署机制以及动态资源提供策略,建立适应性资源协商与竞价模型,实现自动化的虚拟化资源管理,保证服务交付的服务质量要求,并且提高整体的资源使用率。
本文主要的创新点为：
1)针对资源池中集中式管理存在的性能瓶颈和单点失效问题,提出一种基于Gossip协议的混合多属性覆盖网(Gossip-based Hybrid Multi-attribute Overlay, GHMO)拓扑结构,以对等的方式构建了自组织的资源管理基础设施。混合多属性覆盖网同时维护了结构化拓扑和非结构化拓扑,采用混合路由的模式实现了多属性精确查询和多属性范围查询,提高了资源查询的效率。在此基础上,考虑网络时延对资源查询的影响,构建了权值覆盖网的模型,并设计了最近邻居选择策略,通过筛选网络距离较近的邻居,优化了路由的路径选择。仿真结果表明,相比于多属性混合拓扑MAHO, GHMO的资源查询效率提高了33%,同时通过引入最近邻居选择策略,GHMO的路由开销减少了14.2%。
2)针对资源池中多种类型资源分配不均而导致的服务器蔓延现象和能耗偏高问题,提出异构资源池环境下一种能耗感知的启发式向量部署机制PHVP,充分考虑物理机各维度的容量和虚拟机的资源需求特性,通过多维资源协同实现虚拟机的有效放置,以达到服务器整合以及降低能耗的目的。PHVP部署机制采用元启发式算法GRASP改善启发式算法存在的局部优化问题,实现虚拟机的高效放置,并采用能耗感知的主机开启算法优化异构资源池中的物理机使用。仿真结果表明,在异构资源池环境下,相比于传统向量装箱算法和启发式算法,PHVP算法节省了10%的物理机开启数量以及10.3%的总能耗。
3)针对以截止时间为约束的高并发计算任务的服务质量要求,提出了一种面向截止时间约束的动态资源提供策略,通过动态调整资源池中虚拟机的数量以及任务队列中的任务执行顺序,实现动态资源提供以适应不断变化的请求负载规模,以满足各种优先级别任务调度要求。该资源提供策略基于排队论建立面向计算任务的弹性资源供给模型ERPM,周期性根据任务队列的信息调整资源池虚拟机数量,根据WFQBS算法计算每个优先级别的权值,同时结合SLA违例开销调度任务队列中的任务出队顺序。仿真实验结果表明,ERPM具有良好的弹性资源提供能力,并且相比于FCFS、SJF和EDF调度算法,WFQBS调度算法能公平调度不同优先级别的任务,减少了SLA违例开销。
4)针对服务资源市场中资源提供者和资源消费者异构的资源供需状态以及资源交易目标,提出了基于组合／多数量双向拍卖的资源竞价模型BMDA,以买卖双方竞价形式来决定资源分配方案,充分激励资源市场参与者的积极性,以最大化社会效用。在此基础上,提出了两种拍卖机制,基于线性规划的拍卖机制BMDA-LP和基于贪心算法的拍卖机制BMDA-GREEDY,并分析了两种拍卖机制的策略性防伪、预算平衡和个人理性等三种特性。仿真实验结果表明,这两种拍卖机制都能根据资源供需情况进行市场调节功能,并且BMDA-LP的拍卖效率好于BMDA-GREEDY,社会化效用、用户满意度和资源使用率分别提高了161%、70%和37%。
With the matures of service computing and the emergence of new service paradigms, such as mobile internet, Internet of Things and Software as a Service, service systems have evolved to multi-tenant complicated system with high concurrent and multiple device access. Meanwhile, most service systems have been deployed on exclusive infrastructure and the expansion of service systems rely on the unceasing infrastructure investment, which decreases resource utilization and increases the operation cost of service systems. Service systems require more automatic mechanism to consolidate various resources and more elastic strategy to schedule resource, in order to increase total resource utilization, meet the QoS requirement of various service components and improve the efficiency of service delivery
Cloud Computing is the significant transformation of IT service provisioning paradigm. In this paradigm, service and data are stored in scalable shared resource pool via virtualization technology, and can be elastically provisioned to adapt to users' demand, which introduced novel delivery solution for the design, development and deploy of service systems. Based on virtualization technology and the concept of autonomic computing, this paper studies adaptive resource management for service systems. We propose the architecture of adaptive resource management and study four related issues, that is, resource discovery, resource deployment, resource provision and resource negotiation. We construct self-organization collaborated resource overlay, design efficient virtual machine placement mechanism and dynamic resource provisioning strategy, and build up adaptive resource negotiation and bidding model, to achieve automatic virtual resource management. The proposed resource management architecture would guarantee the quality of service delivery and improve total resource utilization.
The major contribution of this thesis is as following:
1) According to bottleneck of performance and point of failure introduced by centralized management of resource pool, we propose a Gossip-based Hybrid Multi-attribute Overlay (GHMO) to construct self-organization resource infrastructure with peer-to-peer fashion. GHMO maintain both of structured and unstructured topology, and use hybrid routing algorithm to implement multi-attribute accurate query and range query, which would raise the efficiency of resource query. Meanwhile, a weight overlay is introduced in the case of network latency, and a nearest neighbor selection strategy is raised to optimize the choice of query routing by selecting neighbor nearby to transmit query. Simulation results show that, compared with Multi-Attribute Hybrid Overlay (MAHO), query efficiency of GHMO is increased by33%. Also, the routing cost is reduced by14.2%with nearest neighbor selection strategy.
2) To deal with server sprawl and high energy consumption caused by unbalanced allocation of multiple resources in resource pool, we propose Power-aware Heuristic Vector Placement (PHVP) mechanism in heterogeneous cloud scenarios. The proposed mechanism considers multiple dimensions of the capacity of physical machines and the demand of virtual machines (VM), and utilizes multiple dimension collaboration to place virtual machines, in order to achieve server consolidation and reduce energy consumption. PHVP apply Greedy Randomized Adaptive Search Procedures (GRASP) to reduce of local optima introduced by classical heuristic methods and improve the efficiency of VM placement. Also, power-aware best-fit host activation algorithm is introduced to optimize the usage of physical machines in heterogeneous resource pool. Simulation results show that, in heterogeneous resource pool, PHVP outperforms FFD and vector based approach, decreasing amount of physical machines and total power consumption by10%and10.3%respectively.
3) To satisfy the demand of high concurrent and deadline constraint computing tasks, we propose a dynamic resource provisioning strategy for deadline constraint tasks. The proposed strategy adjusts the amount of virtual machines in resource pool and the order of task execution dynamically, to adapt to varying request workload and fulfill different level of QoS. We design analytical provision model Elastic Resource Provisioning Model (ERPM) for adaptive provision based on queuing theory, by adjusting the amount of virtual machines in resource pool periodically based on the status of task queue. Also, we schedule the order of task execution based on the weight of each priority calculated by Weighted Fair Queuing (WFQ) algorithm and SLA violation cost. Simulation results show that, ERPM give elastic resource provisioning for dynamic workload. Also, compared with FCFS, SJF and EDF, WFQBS algorithm could fairly schedule tasks with different priority, and reduce the total cost of SLA violation.
4) To cope with various demand/supply amounts and different trade targets of resource provider and resource consumer in service resource market, we propose resource bidding model based on Bundle Multiunit Double Auction (BMDA). The bidding model would determine resource allocation scheme by bidding among buyer and seller, to inspire trade enthusiasm of market participants and maximum social utility. Based on this model, we propose two auction mechanism, linear programming based mechanism BMDA-LP and greedy algorithm based mechanism BMDA-GREEDY. These two auction mechanisms are proved to be strategy-proof, budget-balanced and individual rational. Simulation results show that, two auction mechanisms could regulate auction market adapted to resource demand/supply. Moreover, the auction efficiency of BMDA-LP outperforms that of BMDA-GREEDY, with the growth of161%,70%and37%in social utility, user satisfaction degree and resource utilization respectively
引文
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