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.     

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...     

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.     

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.     

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.     

Improving clustering by learning a bi-stochastic data similarity matrix

An idealized clustering algorithm seeks to learn a cluster-adjacency matrix such that, if two data points belong to the same cluster, the corresponding entry would be 1; otherwise, the entry would be 0. This integer (1/0) constraint makes it difficult to find the optimal solution. We propose a relaxation on the cluster-adjacency matrix, by deriving a bi-stochastic matrix from a data similarity (e.g., kernel) matrix according to the Bregman divergence. Our general method is named the Bregmanian Bi-Stochastication (BBS) algorithm. We focus on two popular choices of the Bregman divergence: the Euclidean distance and the Kullback?CLeibler (KL) divergence. Interestingly, the BBS algorithm using the KL divergence is equivalent to the Sinkhorn?CKnopp (SK) algorithm for deriving bi-stochastic matrices. We show that the BBS algorithm using the Euclidean distance is closely related to the relaxed k-means clustering and can often produce noticeably superior clustering results to the SK algorithm (and other algorithms such as Normalized Cut), through extensive experiments on public data sets.     

插件虚拟机的设计与实现

在基于.Net平台的信息系统开发过程中,组件-控件的复用已经很常见,但是,逻辑流程部分的代码始终没有得到有效的缩减。针对这种现状,提出对插件虚拟机的研究。在组件-控件和功能模块间抽象一层插件,将信息系统划分为不同的插件,通过XML配置文件来定义插件间的逻辑关系,同时,插件也可以很好地实现功能的复用。插件虚拟机用于解释执行该配置文件。     

The Racket virtual machine and randomized testing

We present a PLT Redex model of a substantial portion of the Racket virtual machine and bytecode verifier (formerly known as MzScheme), along with lessons learned in developing the model. Inspired by the “warts-and-all” approach of the VLISP project, in which Wand et al. produced a verified implementation of Scheme, our model reflects many of the realities of a production system. Our methodology departs from the VLISP project’s in its approach to validation; instead of producing a proof of correctness, we explore the use of QuickCheck-style randomized testing, finding it a cheap and effective technique for discovering a variety of errors in the model—from simple typos to more fundamental design mistakes.     

Server consolidation using performance modeling

Server consolidation is crucial to improving a corporation's bottom line by providing savings in hardware, software, maintenance, facility, and labor costs. In today's cost-cutting environment, consolidation efforts can provide significant savings in IT expenditures. Overall potential consolidation benefits include: lower total cost of ownership, improved service levels and availability, and reduced business risks, resulting from consistent business management and resiliency. To fully realize consolidation benefits, businesses must focus on the performance and service levels provided to users in addition to reducing unused capacity. We present a new way to analyze consolidation alternatives using performance modeling and stepwise refinement, a methodology for efficient and effective modeling.     

基于聚类欠采样的极端学习机

针对极端学习机算法对不平衡数据分类问题的处理效果不够理想,提出了一种基于聚类欠采样的极端学习机算法。新算法首先对训练集的负类样本进行聚类生成不同的簇,然后在各簇中按规定的采样率对其进行欠采样,取出的样本组成新的负类数据集,从而使训练集正负类数据个数达到相对平衡,最后训练分类器对测试集进行测试。实验结果表明,新算法有效地降低了数据的不平衡对分类准确率的影响,具有更好的分类性能。     

Experience with an object-oriented virtual machine

The implementation of a virtual machine for X2, an object-based programming language, is presented. The X2 virtual machine, is similar to the Smalltalk-80 virtual machine, but X2 does not perform message look-up. The implementation differs from most Smalitalk-80 systems in that objects are paged and in the ability to save changes to an image instead of the whole image. These differences allow larger images to be handled.