基于负载不确定性的虚拟机整合方法

物理主机工作负载的不确定性容易造成物理主机过载和资源利用率低,从而影响数据中心的能源消耗和服务质量。针对该问题,通过分析物理主机的工作负载记录与虚拟机资源请求的历史数据,提出了基于负载不确定性的虚拟机整合（WU-VMC）方法。为了稳定云数据中心各主机的工作负载,该方法首先利用虚拟机的资源请求拟合物理主机工作负载,并利用梯度下降方法计算虚拟机与物理主机的虚拟机匹配度;然后,利用匹配度进行虚拟机整合,从而解决负载不确定造成的能耗增加和服务质量下降等问题。仿真实验结果表明,WU-VMC方法降低了数据中心的能源消耗,减少了虚拟机迁移次数,提高了数据中心的资源利用率及服务质量。     

Improving performance of similarity-based clustering by featureweight learning

Similarity-based clustering is a simple but powerful technique which usually results in a clustering graph for a partitioning of threshold values in the unit interval. The guiding principle of similarity-based clustering is "similar objects are grouped in the same cluster." To judge whether two objects are similar, a similarity measure must be given in advance. The similarity measure presented in the paper is determined in terms of the weighted distance between the features of the objects. Thus, the clustering graph and its performance (which is described by several evaluation indices defined in the paper) will depend on the feature weights. The paper shows that, by using gradient descent technique to learn the feature weights, the clustering performance can be significantly improved. It is also shown that our method helps to reduce the uncertainty (fuzziness and nonspecificity) of the similarity matrix. This enhances the quality of the similarity-based decision making     

Improving network systems performance by clustering distributed database sites

Clustering network sites is a vital issue in parallel and distributed database systems DDBS. Grouping distributed database network sites into clusters is considered an efficient way to minimize the communication time required for query processing. However, clustering network sites is still an open research problem since its optimal solution is NP-complete. The main contribution in this field is to find a near optimal solution that groups distributed database network sites into disjoint clusters in order to minimize the communication time required for data allocation. Grouping a large number of network sites into a small number of clusters effectively increases the transaction response time, results in better data distribution, and improves the distributed database system performance. We present a novel algorithm for clustering distributed database network sites based on the communication time as database query processing is time dependent. Extensive experimental tests and simulations are conducted on this clustering algorithm. The experimental and simulation results show that a better network distribution is achieved with significant network servers load balance and network delay, a minor communication time between network sites is realized, and a higher distributed database system performance is recognized.     

An energy-aware heuristic framework for virtual machine consolidation in Cloud computing

Virtual machine (VM) consolidation in Cloud computing provides a great opportunity for energy saving. However, the obligation of providing suitable quality of service to end users leads to the necessity in dealing with energy-performance tradeoff. In this paper, we propose a redesigned energy-aware heuristic framework for VM consolidation to achieve a better energy-performance tradeoff. There are two main contributions in the framework: (1) establish a service level agreement (SLA) violation decision algorithm to decide whether a host is overload with SLA violation; (2) minimum power and maximum utilization policy is then proposed to improve the Minimum Power policy in previous work. Finally, we have evaluated our framework through simulation on large-scale experiments driven by workload traces from more than a thousand VMs, and the results show that our framework outperforms previous work. Specifically, it guarantees 21–34 % decrease in energy consumption, 84–92 % decrease in SLA violation, 87–94 % decrease in energy-performance metric, and 63 % decrease in execution time. And we further discuss why the redesigned framework outperforms the previous design.     

基于K最近邻回归预测的高能效虚拟机合并

虚拟机合并和迁移仅考虑当前负载会导致过多非必要迁移,为此,提出基于资源利用预测的虚拟机合并算法UP-BFD.通过K最近邻回归方法同时对主机和虚拟机的负载进行预测,在虚拟机迁移源主机和目标主机的选择上,同步考虑当前超载和预测超载问题,较好避免无用虚拟机迁移.通过随机负载和现实负载进行仿真测试,测试结果表明,UP-BFD算...     

Exploiting ensemble techniques for automatic virtual machine clustering in cloud systems

Cloud computing has recently emerged as a new paradigm to provide computing services through large-size data centers where customers may run their applications in a virtualized environment. The advantages of cloud in terms of flexibility and economy encourage many enterprises to migrate from local data centers to cloud platforms, thus contributing to the success of such infrastructures. However, as size and complexity of cloud infrastructures grow, scalability issues arise in monitoring and management processes. Scalability issues are exacerbated because available solutions typically consider each virtual machine (VM) as a black box with independent characteristics, which is monitored at a fine-grained granularity level for management purposes, thus generating huge amounts of data to handle. We claim that scalability issues can be addressed by leveraging the similarity between VMs in terms of resource usage patterns. In this paper, we propose an automated methodology to cluster similar VMs starting from their resource usage information, assuming no knowledge of the software executed on them. This is an innovative methodology that combines the Bhattacharyya distance and ensemble techniques to provide a stable evaluation of similarity between probability distributions of multiple VM resource usage, considering both system- and network-related data. We evaluate the methodology through a set of experiments on data coming from an enterprise data center. We show that our proposal achieves high and stable performance in automatic VMs clustering, with a significant reduction in the amount of data collected which allows to lighten the monitoring requirements of a cloud data center.     

The effects of scheduling, workload type and consolidation scenarios on virtual machine performance and their prediction through optimized artificial neural networks

The aim of this paper is to study and predict the effect of a number of critical parameters on the performance of virtual machines (VMs). These parameters include allocation percentages, real-time scheduling decisions and co-placement of VMs when these are deployed concurrently on the same physical node, as dictated by the server consolidation trend and the recent advances in the Cloud computing systems. Different combinations of VM workload types are investigated in relation to the aforementioned factors in order to find the optimal allocation strategies. What is more, different levels of memory sharing are applied, based on the coupling of VMs to cores on a multi-core architecture. For all the aforementioned cases, the effect on the score of specific benchmarks running inside the VMs is measured. Finally, a black box method based on genetically optimized artificial neural networks is inserted in order to investigate the degradation prediction ability a priori of the execution and is compared to the linear regression method.     

A reliable energy-aware approach for dynamic virtual machine consolidation in cloud data centers

The Journal of Supercomputing - To achieve energy efficiency in data centers, dynamic virtual machine (VM) consolidation as a key technique has become increasingly important nowadays due to the...     

Improving the performance of distributed virtual environment systems

The last years have witnessed a dramatic growth in the number as well as in the variety of distributed virtual environment systems. These systems allow multiple users, working on different client computers that are interconnected through different networks, to interact in a shared virtual world. One of the key issues in the design of scalable and cost-effective DVE systems is the partitioning problem. This problem consists of efficiently assigning the existing clients to the servers in the system and some techniques have been already proposed for solving it. This paper experimentally analyzes the correlation of the quality function proposed in the literature for solving the partitioning problem with the performance of DVE systems. Since the results show an absence of correlation, we also propose the experimental characterization of DVE systems. The results show that the reason for that absence of correlation is the nonlinear behavior of DVE systems with regard to the number of clients in the system. DVE systems reach saturation when any of the servers reaches 100 percent of CPU utilization. The system performance greatly decreases if this limit is exceeded in any server. Also, as a direct application of these results, we present a partitioning method that is targeted to keep all the servers in the system below a certain threshold value of CPU utilization, regardless of the amount of network traffic. Evaluation results show that the proposed partitioning method can improve DVE system performance, regardless of both the movement pattern of clients and the initial distribution of clients in the virtual world.     

基于混合并发模式的Dalvik虚拟机性能优化

为了改进Dalvik虚拟机的性能,提出了一种基于多线程调度机制的Java虚拟机混合并发模式。该模式利用多线程并发调度和热方法表,通过将Java字节码的编译与执行过程相重叠来提高程序的执行效率,进而提升Dalvik虚拟机的处理速度;并对该模式设计与实现的关键技术进行了分析。实验结果表明,混合并发模式能够有效地提高Dalvik虚拟机中Java程序的执行速度。     

基于虚拟机技术的高性能手机游戏平台

介绍了基于虚拟机的高性能手机游戏平台vGame的架构,以及运行于J2ME环境的虚拟机vMachine和基于Python语法的高效动态脚本vScript,给出了vMachine和vScript的设计原理和实现方案.     

Correction to: A reliable energy-aware approach for dynamic virtual machine consolidation in cloud data centers

The Journal of Supercomputing - The spelling of Monireh H. Sayadnavard’s family name was incorrect.     

对超粒度混杂技术的改进：基于瘦虚拟机的指令集交替技术

超粒度混杂技术对于小型解密程序效率低下,为此提出了基于瘦虚拟机的指令集交替技术。该技术使用一个自动机来记录程序加密和解密的方法,并且使用瘦虚拟机来完成对加密过的程序解释执行。测试结果表时,该技术在保证加密强度的条件下,对效率有较大的提高。     

节能及信任驱动的虚拟机资源调度

针对节能机制和信任驱动的资源调度机制相分离的特点,提出了一种应用到云计算数据中心中的节能及信任驱动的虚拟机资源调度TD energy-aware-Opt算法。该算法利用任务和虚拟机资源之间的信任机制进行任务和虚拟机资源之间的匹配,并通过最小化迁移算法对虚拟机进行实时迁移,以达到保证用户任务性能和数据中心节能的目的。对该算法进行大规模和多角度的仿真实验,结果表明:该算法与传统的基于信任驱动的最小完成时间TD min-min算法、基于信任驱动的最大完成时间TD max-min算法相比,能节省大量电能并且具有较优的平均信任效益、总信任效益和较低的服务等级协议违反率。     

通过标点恢复提高机器同传效果

在机器同传（MSI）流水线系统中,将自动语音识别（ASR）的输出直接输入神经机器翻译（NMT）中会产生语义不完整问题,为解决该问题,提出基于BERT（Bidirectional Encoder Representation from Transformers）和Focal Loss的模型。首先,将ASR系统生成的几个片段缓存并组成一个词串;然后,使用基于BERT的序列标注模型恢复该词串的标点符号,并利用Focal Loss作为模型训练过程中的损失函数来缓解无标点样本比有标点样本多的类别不平衡问题;最后,将标点恢复后的词串输入NMT中。在英-德和汉-英翻译上的实验结果表明,在翻译质量上,使用提出的标点恢复模型的MSI,比将ASR输出直接输入NMT的MSI分别提高了8.19 BLEU和4.24 BLEU,比使用基于注意力机制的双向循环神经网络标点恢复模型的MSI分别提高了2.28 BLEU和3.66 BLEU。因此所提模型可以有效应用于MSI中。     

虚拟机动态迁移方法

对虚拟机动态迁移方法的研究背景、研究意义以及研究现状进行综述,分别介绍了内存预拷贝迁移、内存后拷贝迁移、内存混合复制迁移和基于日志跟踪重现的迁移方法,对其算法思想、关键技术、实现机制以及性能等进行概括、分析和比较,并针对当前主流的预拷贝迁移的优化策略予以分类阐述。最后,对动态迁移方法有待深入的研究热点和发展趋势进行展望。     

A programmable network virtual machine

The increasing diversity of resources which is becoming available to users through the introduction of computer networks has opened up new areas of investigation. One of the problems encountered is that of providing uniform and common access procedures where functionally similar facilities exist on different systems. A lack of standardization in this area can inhibit the very benefits derived through the use of networks. Another aspect is the possibility of achieving genuine resource sharing among several different systems in the network. This can lead to the development of virtual systems that embody the most desirable characteristics of the various real component systems.One approach to solving these problems is to create an agent that is able both to automate remote access procedures on the user's behalf, and provide him with the most appropriate network interface. This paper describes the design of a multi-user virtual machine that is able to perform this role. The machine is programmable by a high-level language which has been specially designed to meet the requirements of a network access environment. It is conjectured that the use of a special purpose high-level language is a most flexible and powerful means of generating particular network agents and that this technique permits the automation of a wide variety of network access procedures.     

Virtual machine consolidation based on interference modeling

Server consolidation is very attractive for cloud computing platforms to improve energy efficiency and resource utilization. Advances in multi-core processors and virtualization technologies have enabled many workloads to be consolidated in a physical server. However, current virtualization technologies do not ensure performance isolation among guest virtual machines, which results in degraded performance due to contention in shared resources along with violation of service level agreement (SLA) of the cloud service. In that sense, minimizing performance interference among co-located virtual machines is the key factor of successful server consolidation policy in the cloud computing platforms. In this work, we propose a performance model that considers interferences in the shared last-level cache and memory bus. Our performance interference model can estimate how much an application will hurt others and how much an application will suffer from others. We also present a virtual machine consolidation method called swim which is based on our interference model. Experimental results show that the average performance degradation ratio by swim is comparable to the optimal allocation.