虚拟机在线迁移性能优化关键技术研究
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摘要
虚拟机在线迁移可以无缝地把基于虚拟机运行的操作系统不中断地从一台物理机器上迁移到另一台物理机器上运行,该技术广泛应用于负载均衡、能耗管理、系统容错及软硬件的在线维护等场景下。虚拟机迁移作为虚拟化的核心关键技术之一对于建立可动态伸缩、资源集约的大型绿色数据中心具有重要意义。
目前,国内外针对虚拟机迁移技术的研究方心未艾,尚存在一些亟待深入探究的问题。首先,目前虚拟机的迁移缺乏模型化的性能预测与评价方法来指导迁移决策；其次,虚拟机检查点作为迁移的支撑技术,由于保存的是整个操作系统,因而粒度较大,开销较大,尚需要优化方法使其轻量化、透明化；再次,基于内存预拷贝的传统迁移算法不能适用于所有的应用场景,特别是内存密集型的应用很可能使预拷贝算法失效；最后,虚拟机迁移的性能主要取决于应用的特性和迁移算法的选择,目前尚缺乏一种综合的决策机制来自适应地匹配不同的迁移算法和不同的负载类型及应用场景。针对这些问题,以虚拟机在线迁移的性能优化为中心,从四个侧面对虚拟机迁移的关键技术和应用进行广泛深入的研究。
提出虚拟机在线迁移的性能和能耗预测模型。由于虚拟机中运行的应用程序及负载的多样性,导致迁移不同虚拟机的代价存在着巨大的差异性。当前对虚拟机迁移的放置策略的研究主要集中于迁移的触发条件和目的地的选择问题上,而缺乏对迁移本身代价的关注。在深入分析“预拷贝”算法中影响虚拟机迁移性能的关键参数的基础上,结合理论和实验,利用线性回归的学习方法可以建立虚拟机迁移性能和能耗的代价预测模型。该模型在虚拟机管理器层收集虚拟机负载的相关信息,并模拟“预拷贝”算法的执行,从而可以定量地预测虚拟机迁移的关键性能指标,为虚拟机的选择算法提供决策支撑。该模型可以作为迁移决策的评判标准之一,是优化设计虚拟机放置算法的基础,同时也可以指导对迁移算法本身的性能优化。
针对虚拟机检查点粒度大,开销大的问题,提出内存空洞排除技术、写时复制及增量机制三种关键技术来优化虚拟机检查点的性能,并实现轻量级的虚拟机在线检查点系统VMckpt。通过内存压缩及空洞排除技术可以避免保存虚拟机内存镜像中的无用信息,大大减小虚拟机检查点的大小,从而减小了检查点保存和恢复的时空开销；通过写时复制机制,在保存检查点时无须中断虚拟机的执行,可以大幅减小检查点停机时间；通过增量检查点保存机制,避免了频繁保存检查点造成的大量数据冗余,大幅减小保存检查点的时空开销。
提出基于在线检查点和全系统不确定性事件记录/回放技术的虚拟机迁移算法CR/TR-Motion。由于广泛采用的预拷贝算法在虚拟机迁移的同步过程中需要重复拷贝的数据粒度是内存脏页面,导致网络带宽消耗较大,迁移的总时间较长,对于内存密集型应用停机时间也较长。CR/TR-Motion算法在虚拟机迁移的过程中,记录源虚拟机中发生的所有不确定性事件,并利用这些日志信息在目的主机上进行回放,从而来同步迁移中的源虚拟机和目的虚拟机之间的运行时状态。山于日志的数据量远远小于内存预拷贝算法中需要拷贝的脏页面,该方法与内存预拷贝算法相比,能更大限度地减小虚拟机的停机时间、总的迁移时间和网络数据传输量。同时CR/TR-Motion算法由于同步过程中网络带宽消耗较小,非常适宜于广域网的虚拟机在线迁移。
提出应用特征感知的自适应迁移算法选择决策模型,使负载和迁移算法之间总能最佳适配。选取了三种典型的虚拟机迁移算法纳入到该决策框架下,分别是预拷贝(pre-copy)算法、后拷贝(post-copy)算法以及CR/TR-Motion算法。通过建立这三种虚拟机迁移算法的性能预测模型,并对负载的应用特征进行在线提取和分析,基于模糊综合评判方法来实现不同应用和迁移算法之间的最佳适配。通过典型的实例分析,验证了模糊综合决策理论应用于虚拟机迁移算法的选择总可以使迁移的性能达到最优。
综上所述,在不同层次对虚拟机在线迁移的算法和决策模型提出的优化策略,可以有效减小虚拟机在线迁移的代价,使得迁移性能得到大幅提升。
Live migration of virtual machines (VM) can seamlessly and transparently relocate a VM from physical machine to another machine. It is widely used for load balancing, power saving, fault tolerance and online maintenance, thus provides a significant benefit for administrators of data centers and clusters. VM live migration has became a key technology of virtualization, it provide significant advantages to construct flexible, scalable, and resource-efficiency large green datacenters. The research on virtual machine live migration is emerging in recent years, however, it have quickly attracted a lot of interest in research comites of virtualization technology. By now, there are still many issues that need to be further studied. First, there is still lack of performance prediction model for VM migration to guide migration decision making. Second, VM checkpointing, the technical support for VM migration, need to save a whole operation system, so the cost is relatively high. It needs optimizing techniques to develop a lightweight and transparent checkpointing mechanism. Third, the widely used pre-copying algorithm is not suit for all scenarios, for example, a memory intensive workload may lead to pre-copy inapplicable. At last, the performance of VM migration is mainly determined by the characteristic of applications and the selection of migration algorithms. There is still lack of a synthesized decision-making scheme to adaptively map different applications and different migration algorithms. This dissertation targets to the performance optimization of VM live migration, studies the key technologies of VM migration from four aspects.
First, we propose the performance and energy prediction models for VM live migration. As migration costs may vary significantly for different workloads due to the variety of VM configurations and workload characteristics. Current study on VM placement mostly focus on the issue of trigger conditions (when) and destination selections (where), however, migration decision-making still lack model-guided methodologies to predict the migration cost. To make an optimal migration decision, we investigate design methodologies to minimize the migration overhead by quantitatively predicting the migration performance and energy cost. We thoroughly analyze the key parameters that affect the migration cost from theory to practice, and design two application-oblivious models for the cost prediction by using learning technique such as linear regression. To the best of our knowledge, this is the first kind of work to estimate VM live migration cost in terms of both performance and energy in a quantitative approach. The models should be one of criterias for migration decision making and foundation for algorithm design of VM placement. It can also guide the performance optimazation of VM live migration algorithm.
Second, we propose free page evication, copy-on-write and increamental mechanism to optimize the performance of VM checkpointing, and implement a prototype for lightweight and live VM checkpointing, called VMckpt. Because the virtualization layer has little semantic knowledge about the operation system and the applications running atop, VM-layer checkpointing requires saving the entire operating system state rather than a single process. The overhead may render the approach impractical. To reduce the size of VM checkpoint, in this paper we propose a page eviction scheme and an incremental checkpointing mechanism to avoid saving unnecessary VM pages in the checkpoint. This scheme can significantly reduce the size of VM checkpoint, and thus decrease the time and space cost for saving and restoring a VM checkpoint. To keep the VM online transparently, we propose a live checkpointing mechanism by saving the memory image in a Copy-On-Write (COW) manner. The checkpointing downtime can be reduced to an imperceptible level.
Third, we propose a novel algorithm of VM live migration based on VM live checkpointing and full-system logging/replay techiques, called CR/TR-Motion. Although previous memory-to-memory approaches demonstrate the effectiveness of live VM migration in local area networks (LAN), they are not applicable for all workloads and scenarios, especially for some memory-intensive workloads. Moreover, they would cause a long period of downtime in wide area network (WAN) environments. In this paper, we design and implement a novel approach, namely CR/TR-Motion, which adopts checkpointing/recovery and trace/replay technologies to provide fast, transparent VM migration for both LAN and WAN environments. In contrast to pre-copying algorithm, CR/TR-Motion synchronizes the migrated VM's state using execution trace logged on the source host instead of dirty memory pages, and the destination host replays the logs till the target VM gets a consistent state with the source VM. As the data volume of log transferred during synchronization is much less than dirty pages, CR/TR-Motion can greatly reduce the migration downtime and network bandwidth consumption compared to pre-copy algorithm. Moreover, because CR/TR-Motion algorithm only requires a litter network bandwidth, it is espercially applicable for VM migration in WAN environment.
At last, we propose a decision-making model for adaptive migration algorithm seclection based on application characteristics. The performance of live VM migration is mainly determined by the characteristic of applications and the seclection of migration algorithms. Currently, there is still neck of a synthetic mechanism for migration decision making to adaptively map different migration algorithms and different types of workloads or application scenarioes. After a thorough analysis of features of different migration algorithms, we choose three typical migration algorithms in this model, which are pre-copying, post-copying and CR/TR-Motion. We first construct the performance prediction models for each migration algorithm, and then online sample and analyse the application features, which may directly guide the migration algorithm selection. For some instance, the requirement of migration performance is non-deterministic or vague, we design a mapping algorithm between different applications and migration algorithms based on fuzzy theory. A case study shows that the fuzzy synthetic decision making can always choose the optimal migration algorithm for a typical application.
目前,国内外针对虚拟机迁移技术的研究方心未艾,尚存在一些亟待深入探究的问题。首先,目前虚拟机的迁移缺乏模型化的性能预测与评价方法来指导迁移决策；其次,虚拟机检查点作为迁移的支撑技术,由于保存的是整个操作系统,因而粒度较大,开销较大,尚需要优化方法使其轻量化、透明化；再次,基于内存预拷贝的传统迁移算法不能适用于所有的应用场景,特别是内存密集型的应用很可能使预拷贝算法失效；最后,虚拟机迁移的性能主要取决于应用的特性和迁移算法的选择,目前尚缺乏一种综合的决策机制来自适应地匹配不同的迁移算法和不同的负载类型及应用场景。针对这些问题,以虚拟机在线迁移的性能优化为中心,从四个侧面对虚拟机迁移的关键技术和应用进行广泛深入的研究。
提出虚拟机在线迁移的性能和能耗预测模型。由于虚拟机中运行的应用程序及负载的多样性,导致迁移不同虚拟机的代价存在着巨大的差异性。当前对虚拟机迁移的放置策略的研究主要集中于迁移的触发条件和目的地的选择问题上,而缺乏对迁移本身代价的关注。在深入分析“预拷贝”算法中影响虚拟机迁移性能的关键参数的基础上,结合理论和实验,利用线性回归的学习方法可以建立虚拟机迁移性能和能耗的代价预测模型。该模型在虚拟机管理器层收集虚拟机负载的相关信息,并模拟“预拷贝”算法的执行,从而可以定量地预测虚拟机迁移的关键性能指标,为虚拟机的选择算法提供决策支撑。该模型可以作为迁移决策的评判标准之一,是优化设计虚拟机放置算法的基础,同时也可以指导对迁移算法本身的性能优化。
针对虚拟机检查点粒度大,开销大的问题,提出内存空洞排除技术、写时复制及增量机制三种关键技术来优化虚拟机检查点的性能,并实现轻量级的虚拟机在线检查点系统VMckpt。通过内存压缩及空洞排除技术可以避免保存虚拟机内存镜像中的无用信息,大大减小虚拟机检查点的大小,从而减小了检查点保存和恢复的时空开销；通过写时复制机制,在保存检查点时无须中断虚拟机的执行,可以大幅减小检查点停机时间；通过增量检查点保存机制,避免了频繁保存检查点造成的大量数据冗余,大幅减小保存检查点的时空开销。
提出基于在线检查点和全系统不确定性事件记录/回放技术的虚拟机迁移算法CR/TR-Motion。由于广泛采用的预拷贝算法在虚拟机迁移的同步过程中需要重复拷贝的数据粒度是内存脏页面,导致网络带宽消耗较大,迁移的总时间较长,对于内存密集型应用停机时间也较长。CR/TR-Motion算法在虚拟机迁移的过程中,记录源虚拟机中发生的所有不确定性事件,并利用这些日志信息在目的主机上进行回放,从而来同步迁移中的源虚拟机和目的虚拟机之间的运行时状态。山于日志的数据量远远小于内存预拷贝算法中需要拷贝的脏页面,该方法与内存预拷贝算法相比,能更大限度地减小虚拟机的停机时间、总的迁移时间和网络数据传输量。同时CR/TR-Motion算法由于同步过程中网络带宽消耗较小,非常适宜于广域网的虚拟机在线迁移。
提出应用特征感知的自适应迁移算法选择决策模型,使负载和迁移算法之间总能最佳适配。选取了三种典型的虚拟机迁移算法纳入到该决策框架下,分别是预拷贝(pre-copy)算法、后拷贝(post-copy)算法以及CR/TR-Motion算法。通过建立这三种虚拟机迁移算法的性能预测模型,并对负载的应用特征进行在线提取和分析,基于模糊综合评判方法来实现不同应用和迁移算法之间的最佳适配。通过典型的实例分析,验证了模糊综合决策理论应用于虚拟机迁移算法的选择总可以使迁移的性能达到最优。
综上所述,在不同层次对虚拟机在线迁移的算法和决策模型提出的优化策略,可以有效减小虚拟机在线迁移的代价,使得迁移性能得到大幅提升。
Live migration of virtual machines (VM) can seamlessly and transparently relocate a VM from physical machine to another machine. It is widely used for load balancing, power saving, fault tolerance and online maintenance, thus provides a significant benefit for administrators of data centers and clusters. VM live migration has became a key technology of virtualization, it provide significant advantages to construct flexible, scalable, and resource-efficiency large green datacenters. The research on virtual machine live migration is emerging in recent years, however, it have quickly attracted a lot of interest in research comites of virtualization technology. By now, there are still many issues that need to be further studied. First, there is still lack of performance prediction model for VM migration to guide migration decision making. Second, VM checkpointing, the technical support for VM migration, need to save a whole operation system, so the cost is relatively high. It needs optimizing techniques to develop a lightweight and transparent checkpointing mechanism. Third, the widely used pre-copying algorithm is not suit for all scenarios, for example, a memory intensive workload may lead to pre-copy inapplicable. At last, the performance of VM migration is mainly determined by the characteristic of applications and the selection of migration algorithms. There is still lack of a synthesized decision-making scheme to adaptively map different applications and different migration algorithms. This dissertation targets to the performance optimization of VM live migration, studies the key technologies of VM migration from four aspects.
First, we propose the performance and energy prediction models for VM live migration. As migration costs may vary significantly for different workloads due to the variety of VM configurations and workload characteristics. Current study on VM placement mostly focus on the issue of trigger conditions (when) and destination selections (where), however, migration decision-making still lack model-guided methodologies to predict the migration cost. To make an optimal migration decision, we investigate design methodologies to minimize the migration overhead by quantitatively predicting the migration performance and energy cost. We thoroughly analyze the key parameters that affect the migration cost from theory to practice, and design two application-oblivious models for the cost prediction by using learning technique such as linear regression. To the best of our knowledge, this is the first kind of work to estimate VM live migration cost in terms of both performance and energy in a quantitative approach. The models should be one of criterias for migration decision making and foundation for algorithm design of VM placement. It can also guide the performance optimazation of VM live migration algorithm.
Second, we propose free page evication, copy-on-write and increamental mechanism to optimize the performance of VM checkpointing, and implement a prototype for lightweight and live VM checkpointing, called VMckpt. Because the virtualization layer has little semantic knowledge about the operation system and the applications running atop, VM-layer checkpointing requires saving the entire operating system state rather than a single process. The overhead may render the approach impractical. To reduce the size of VM checkpoint, in this paper we propose a page eviction scheme and an incremental checkpointing mechanism to avoid saving unnecessary VM pages in the checkpoint. This scheme can significantly reduce the size of VM checkpoint, and thus decrease the time and space cost for saving and restoring a VM checkpoint. To keep the VM online transparently, we propose a live checkpointing mechanism by saving the memory image in a Copy-On-Write (COW) manner. The checkpointing downtime can be reduced to an imperceptible level.
Third, we propose a novel algorithm of VM live migration based on VM live checkpointing and full-system logging/replay techiques, called CR/TR-Motion. Although previous memory-to-memory approaches demonstrate the effectiveness of live VM migration in local area networks (LAN), they are not applicable for all workloads and scenarios, especially for some memory-intensive workloads. Moreover, they would cause a long period of downtime in wide area network (WAN) environments. In this paper, we design and implement a novel approach, namely CR/TR-Motion, which adopts checkpointing/recovery and trace/replay technologies to provide fast, transparent VM migration for both LAN and WAN environments. In contrast to pre-copying algorithm, CR/TR-Motion synchronizes the migrated VM's state using execution trace logged on the source host instead of dirty memory pages, and the destination host replays the logs till the target VM gets a consistent state with the source VM. As the data volume of log transferred during synchronization is much less than dirty pages, CR/TR-Motion can greatly reduce the migration downtime and network bandwidth consumption compared to pre-copy algorithm. Moreover, because CR/TR-Motion algorithm only requires a litter network bandwidth, it is espercially applicable for VM migration in WAN environment.
At last, we propose a decision-making model for adaptive migration algorithm seclection based on application characteristics. The performance of live VM migration is mainly determined by the characteristic of applications and the seclection of migration algorithms. Currently, there is still neck of a synthetic mechanism for migration decision making to adaptively map different migration algorithms and different types of workloads or application scenarioes. After a thorough analysis of features of different migration algorithms, we choose three typical migration algorithms in this model, which are pre-copying, post-copying and CR/TR-Motion. We first construct the performance prediction models for each migration algorithm, and then online sample and analyse the application features, which may directly guide the migration algorithm selection. For some instance, the requirement of migration performance is non-deterministic or vague, we design a mapping algorithm between different applications and migration algorithms based on fuzzy theory. A case study shows that the fuzzy synthetic decision making can always choose the optimal migration algorithm for a typical application.
引文
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