Jianhai Chen

Virt-LM: a benchmark for live migration of virtual machine

Virtualization technology has been widely applied in data centers and IT infrastructures, with advantages of server consolidation and live migration. Through live migration, data centers could flexibly move virtual machines among different physical machines to balance workloads, reduce energy consumption and enhance service availability. Todays data centers can grow to a huge scale. This implies that frequent live migration would be desirable for the economic use of hardware resources. Then, the performance of the live migration strategy will be an issue. So, we need a reliant evaluation method to choose the software and hardware environments that will produce the best live migration performance. However, there is not a complete live migration benchmark available currently. In addition, the existing evaluation methodologies select different metrics, different workloads and different test means. Thus, it is difficult to compare their results. In this paper we first survey the current research and their evaluation methods on live migration. We then summarize the critical issues for the live migration evaluation and also raise other unreported potential problems. We propose our solutions and present an implementation in our live migration benchmark -- Virt-LM. This is a benchmark for comparing live migration performance among different software and hardware environments in a data center scenario. We detail its design and provide some experimental results to validate its effectiveness.

vTestkit: A Performance Benchmarking Framework for Virtualization Environments

Virtualization technology has attracted wide attention in recent years as a method to improve resource utilization, reduce costs, and ease server management. However, the performance penalty resulting from virtualization is an unneglectable problem and should be carefully evaluated. To our knowledge, there are few performance evaluating tools developed for virtualization environments. We propose a configurable framework and implement a prototype vTestkit to provide a platform to do performance evaluation for virtualization environments easily, flexibly, and automatically. In this paper, we first discuss the requirements and challenges of performance measurement in virtualization environments, and then present a methodology for characterizing the performance of single virtual machine (VM) scenario and multi-VM scenario. Then we introduce the architecture of vTestkit framework, implement details, and the testing process with vTestkit. Finally, three typical case studies are presented to show that vTestkit can meet the complex testing requirements well and is propitious to various scenarios.

Informed live migration strategies of virtual machines for cluster load balancing

Virtualization technology brings great conveniences to cluster and data center management. By using this technique, we can reconstruct a new computing environment quickly and easily. Compared to the traditional cluster environment, load balancing in a virtualized environment needs to address several new problems. This paper focuses on live migration strategies for load balancing in the virtualized cluster. We first divide the whole balancing process into three sub-problems, namely, the selection of the VM being migrated to, the choice of destination host and the determination of the migration execution sequence. Then we perform a series of experiments to investigate the particular features of the live migration of virtual machines in the balancing scenario. Based on our experiment results, we propose an informed live migration strategy which includes affinity-aware decision making and workload-aware migration to improve the efficiency of configuration of the virtualized cluster.

Evaluate the performance and scalability of image deployment in virtual data center

Virtualization technology plays an important role in modern data center, as it creates an opportunity to improve resource utilization, reduce energy costs, and ease server management. However, virtual machine deployment issues arise when allocating virtual machines into single or multiple physical servers. In this paper, we explore the performance and scalability issues for virtual machine deployment in a virtualized data center. We first evaluate the image scalability when allocating multiple VMs per physical server using four typical servers in data center. Then we investigate how the overall efficiency will be affected when deploying M virtual machines into N physical machines with different deployment strategies. Experimental results show that: (i) There is a resource bottleneck when deploying single type virtual machine server into single physical server, except for composite workloads. (ii) More physical machines do not always benefit for some specific applications to support a fixed number of virtual machines. (iii) MPI and network communication overheads affect the deployment efficiency seriously.

ITC-LM: A Smart Iteration-Termination Criterion Based Live Virtual Machine Migration

Live migration of virtual machines VMs plays an important role in grids, clouds and datacenters, and has become the cornerstone of resource management in virtualized systems. The efficiency of live migration depends on the downtime, total migration time and total transferred data. However, while migrating a memory-intensive VM, XEN/KVM often do many useless iterations of memory copy in order to reach expected downtime which can never be reached, leading to a great deal of useless data transferring and insufferable total migration time. It consumes mass of network bandwidth and CPU resource when transferring memory from one to another node. Hence, a critical task is to determine the optimal time to terminate the copy iteration for live migration. In this paper, we propose a smart iteration-termination criterion based live migration which is termed as ITC-LM, to self adaptively control when to terminate iteration. We have implemented ITC-LM into KVM/QEMU. The improvement is significant, especially when migrate a memory-intensive VM. The experimental results show that, our approach can decrease 50.33% of total transferred data on average without impairing migration downtime.

Virt-LM: a benchmark for live migration of virtual machine (abstracts only)

Virtualization technology has been widely applied in data centers and IT infrastructures, with advantages of server consolidation and live migration. Through live migration, data centers could flexibly move virtual machines among different physical machines to balance workloads, reduce energy consumption and enhance service availability. Todays data centers can grow to a huge scale. This implies that frequent live migration would be desirable for the economic use of hardware resources. Then, the performance of the live migration strategy will be an issue. So, we need a reliant evaluation method to choose the software and hardware environments that will produce the best live migration performance. However, there is not a complete live migration benchmark available currently. In addition, the existing evaluation methodologies select different metrics, different workloads and different test means. Thus, it is difficult to compare their results. In this paper we first survey the current research and their evaluation methods on live migration. We then summarize the critical issues for the live migration evaluation and also raise other unreported potential problems. We propose our solutions and present an implementation in our live migration benchmark -- Virt-LM. This is a benchmark for comparing live migration performance among different software and hardware environments in a data center scenario. We detail its design and provide some experimental results to validate its effectiveness.

A Hot-Page Aware Hybrid-Copy Migration Method

Hybrid-copy migration is a practical mean in cloud computing for memory intensive workload. But it does not perform perfectly in fetching remote pages. Aiming at solving this problem, we present a hot-page hybrid-copy migration method. We design a hot-page capturer to find out hot-pages, and a hot-page syringe to push hot-pages into transmission queue. We present an evaluation called page fault interval time, to evaluate the performance of hybrid-copy migration. The experimental results show that our method can extend the free part in page fault interval time about 19.79%, reduce the amount of remote page faults about 9.6%, and finally improve the performance of hybrid-copy migration.

HASO: A hot-page aware scheduling optimization method in virtualized NUMA systems

In the situation of CPU and memory overcommit, it is inevitable that some NUMA nodes will be overloaded or hotspotted and become hot nodes, leading to the VM application performance degradation in virtualized NUMA (vNUMA) systems. However, the virtual machine monitor (VMM) can not be aware of the NUMA feature and the distribution array of hot memory pages amongst NUMA nodes effectively. Aiming at eliminating the hot nodes and load imbalance in a vNUMA system, this paper proposes a hot-page aware scheduling optimization method (HASO) and implements a HASO scheduling system. At first we monitor the NUMA node load state, find the hot nodes in which we choose the hot VMs that cause the node hotspots. Then, we predict the distribution of future hot memory pages of the hot VM, and evaluate the cost of migrating the hot pages between NUMA nodes. At last, the hot pages of hot VM with minimized migration cost are migrated to idling nodes, so as to eliminate the hot node and improve the VM application performance. In contrast to the default scheduling mechanism of VMM, our HASO scheduling method can not only improve the memory intensive benchmark cg by up to 27.06% and the benchmark stream by up to 15.63%, but also balance the load of NUMA nodes.

DP: Dynamic Prepage in Postcopy Migration for Fixed-Size Data Load

Postcopy migration is a mature technology in virtualization. However the performance of postcopy is not stable. We find many memory intensive loads having a high proportion of independent fixed-size data (FSD) cases. To improve migration performance, we present DP: an algorithm which applies to intelligently tackle FSD load during postcopy migration. We implement DP as an online algorithm triggered by remote paging, and adjusting to prepage the most appropriate amount of pages related to recent page fault records. DP also has a threshold processing mechanism to prevent from noise which is derived from load size fluctuation. The experimental results show that DP algorithm can significantly reduce response time and implicit downtime in postcopy migration, with an high improvement on QoS.