Analyzing the performance of a cluster-based architecture for immersive visualization systems

Cluster computing has become an essential issue for designing immersive visualization systems. This paradigm employs scalable clusters of commodity computers with much lower costs than would be possible with the high-end, shared memory computers that have been traditionally used for virtual reality purposes. This change in the design of virtual reality systems has caused some development environments oriented toward shared memory computing to require modifications to their internal architectures in order to support cluster computing. This is the case of VR Juggler, which is considered one of the most important virtual reality application development frameworks based on open source code.     

Bargaining over multiple issues in finite horizon alternating-offers protocol

In this paper we study multi issue alternating-offers bargaining in a perfect information finite horizon setting, we determine the pertinent subgame perfect equilibrium, and we provide an algorithm to compute it. The equilibrium is determined by making a novel use of backward induction together with convex programming techniques in multi issue settings. We show that the agents reach an agreement immediately and that such an agreement is Pareto efficient. Furthermore, we prove that, when the multi issue utility functions are linear, the problem of computing the equilibrium is tractable and the related complexity is polynomial with the number of issues and linear with the deadline of bargaining.      

Language-theoretic aspects of DNA complematarity

The optimism about the possibilities of DNA computing is based on two central issues: the Watson–Crick complementarity and the massive parallelism of DNA strands. While the latter issue renders exhaustive searches possible and thus may settle problems previously considered intractable, the former issue is the cause behind the universality of many models of DNA computing. Moreover, complementarity can be viewed as a purely language-theoretic operation: undesirable circumstances in a string trigger a transition to the complementary string. This aspect of complementarity is investigated in the present paper, mainly from the point of view of L systems. New types of word sequences will be discovered. Sometimes the resulting decision problems are equivalent to well-known open problems from other areas.     

广义不完备多粒度标记决策系统的粒度选择

粒计算(granular computing, GrC)是知识表示和数据挖掘的一个重要方法,它模拟人类思考模式,以粒为基本计算单位,以建立大规模复杂数据和信息处理的有效计算模型为目标.粒计算主要研究粒的构造、解释、表示、粒度的选择以及用规则形式所描述的粒与粒之间的关系等.针对具有多粒度标记的不完备信息系统的知识获取问题,首先,介绍了广义不完备多粒度标记信息系统的概念,在该信息系统中定义了相似关系,给出了在不同粒度标记层面下信息粒的表示及其相互关系,并定义了基于相似关系的集合的下、上近似概念,给出了近似算子的性质;其次,定义了广义不完备多粒度标记决策系统中的粒度标记选择的概念,阐明了所有粒度标记选择全体构成了一个完备格;最后,讨论了广义不完备多粒度标记决策系统中的最优粒度标记选择问题,并用证据理论中的信任函数和似然函数刻画了协调的不完备多粒度标记决策系统的最优粒度选择特征.     

普及计算环境的设备灵活接入技术研究--基于OSGi的数字家庭解决方案

普及计算使人们能通过各类智能计算设备，随时随地获得各种所需的信息服务。作为普及计算的一个重要的应用领域，数字家庭将家庭内各种家电和设备联网，通过网络为人们提供丰富、个性化、方便、安全和高效的服务。而如何达到各种智能设备的灵活接入，成了实现此目标的一个关键问题。在和。SVA-IBM联合实验室合作iHome项目中，作者对此进行了深入的研究。本文提出了基于OSGi的数字家庭普及计算环境智能计算设备灵活接入解决方案，并实现了一个应用的系统，从而验证其可行性。     

DeepComp: towards a balanced system design for high performance computer systems

Today, cluster-based computing is the mainstream architecture for high end computer systems. Balanced system design is critical for large scale cluster systems to achieve high efficiency. This paper addresses the practice on DeepComp high end computer systems toward a balanced system design. Methodologies of designing balanced large scale cluster systems are given. A method for balancing central processing unit (CPU) and memory hierarchy is addressed. For balancing computing nodes and I/O systems, two approaches are given: maximum bandwidth criterion and maximum number of computing nodes which can concurrently access I/O systems. Experiences of Lenovo high end cluster systems show that above methods are effective. Lenovo strategies toward a balanced system design for both peta and 10 peta scale high productivity computing systems (HPCSs).     

Toward Cloud Computing QoS Architecture: Analysis of Cloud Systems and Cloud Services

Cloud can be defined as a new computing paradigm that provides scalable, on-demand, and virtualized resources for users. In this style of computing, users can access a shared pool of computing resources which are provisioned with minimal management efforts of users. Yet there are some obstacles and concerns about the use of clouds. Guaranteeing quality of service (QoS) by service providers can be regarded as one of the main concerns for companies tending to use it. Service provisioning in clouds is based on service level agreements representing a contract negotiated between users and providers. According to this contract, if a provider cannot satisfy its agreed application requirements, it should pay penalties as compensation. In this paper, we intend to carry out a comprehensive survey on the models proposed in literature with respect to the implementation principles to address the QoS guarantee issue.      

Personal computing vs. personal computers

Most discussion of personal computers in organizations focuses on a peripheral issue: the choice between distributed computer hardware and central computer hardware — between personal computers and mainframes. The real issue has nothing to do with personal computers or other hardware; it is the difference between organizational computing and personal computing in organizational information systems. This paper discusses the differences between organizational computing and personal computing, the connections between the organizational and the personal computing systems within organizations, and the role of MIS.     

Cloud Computing: New Wine or Just a New Bottle?

Cloud computing has evolved from previous computing paradigms going back as far to the days of mainframes, but is it really different? Do the explosive new capabilities from cloud computing solve any of the problems left unsolved from three decades ago? The authors in this issue discuss their views on what cloud computing is leaving the reader to decide for themselves.     

Environment-conscious scheduling of HPC applications on distributed Cloud-oriented data centers

The use of High Performance Computing (HPC) in commercial and consumer IT applications is becoming popular. HPC users need the ability to gain rapid and scalable access to high-end computing capabilities. Cloud computing promises to deliver such a computing infrastructure using data centers so that HPC users can access applications and data from a Cloud anywhere in the world on demand and pay based on what they use. However, the growing demand drastically increases the energy consumption of data centers, which has become a critical issue. High energy consumption not only translates to high energy cost which will reduce the profit margin of Cloud providers, but also high carbon emissions which are not environmentally sustainable. Hence, there is an urgent need for energy-efficient solutions that can address the high increase in the energy consumption from the perspective of not only the Cloud provider, but also from the environment. To address this issue, we propose near-optimal scheduling policies that exploit heterogeneity across multiple data centers for a Cloud provider. We consider a number of energy efficiency factors (such as energy cost, carbon emission rate, workload, and CPU power efficiency) which change across different data centers depending on their location, architectural design, and management system. Our carbon/energy based scheduling policies are able to achieve on average up to 25% of energy savings in comparison to profit based scheduling policies leading to higher profit and less carbon emissions.     

基于区间二型模糊集合的语言动力系统稳定性

运用区间二型模糊集合(Interval type-2 fuzzy sets, IT2 FSs) 的扩展原理将常规的数值函数转化为对应的区间二型模糊函数, 并给出了相应的词计算(Computing with words, CW)方法与算法,最后分析了严格单调情况下基于区间二型模糊集合的单输入单输出系统的语言动力系统(Linguistic dynamic systems, LDS)稳定性.     

Superpipelined high-performance optical-flow computation architecture

Optical-flow computation is a well-known technique and there are important fields in which the application of this visual modality commands high interest. Nevertheless, most real-world applications require real-time processing, an issue which has only recently been addressed. Most real-time systems described to date use basic models which limit their applicability to generic tasks, especially when fast motion is presented or when subpixel motion resolution is required. Therefore, instead of implementing a complex optical-flow approach, we describe here a very high-frame-rate optical-flow processing system. Recent advances in image sensor technology make it possible nowadays to use high-frame-rate sensors to properly sample fast motion (i.e. as a low-motion scene), which makes a gradient-based approach one of the best options in terms of accuracy and consumption of resources for any real-time implementation. Taking advantage of the regular data flow of this kind of algorithm, our approach implements a novel superpipelined, fully parallelized architecture for optical-flow processing. The system is fully working and is organized into more than 70 pipeline stages, which achieve a data throughput of one pixel per clock cycle. This computing scheme is well suited to FPGA technology and VLSI implementation. The developed customized DSP architecture is capable of processing up to 170 frames per second at a resolution of 800 × 600 pixels. We discuss the advantages of high-frame-rate processing and justify the optical-flow model chosen for the implementation. We analyze this architecture, measure the system resource requirements using FPGA devices and finally evaluate the system’s performance and compare it with other approaches described in the literature.     

Cloud Control Systems

The concept of cloud control systems is discussed in this paper, which is an extension of networked control systems (NCSs). With the development of internet of things (IOT), the technology of NCSs has played a key role in IOT. At the same time, cloud computing is developed rapidly, which provides a perfect platform for big data processing, controller design and performance assessment. The research on cloud control systems will give new contribution to the control theory and applications in the near future.      

Daonity – Grid security from two levels of virtualization

The service oriented architecture of grid computing has been thoughtfully engineered to achieve a service level virtualization: not only should a grid be a virtual machine (also known as a virtual organization, VO) of unbounded computational power and storage capacity, but also should the virtual machine be serviceable in all circumstances independent from serviceability of any of its component. At present, a grid VO as a result of service level virtualization only is more or less confined to participants from scientific computing communities, i.e., can have a limited scale. It is widely agreed that for a grid to pool resources of truly unbounded scale, commercial enterprises and in particular server-abundant financial institutions, should also “go for the grid,” i.e., open up their servers for being used by grid VO constructions. We believed that it is today's inadequate strength of the grid security practice that is the major hurdle to prevent commercial organizations from serving and participating the grid.This article presents the work of Daonity which is our attempt to strengthening grid security. We identify that a security service which we name behavior conformity be desirable for grid computing. Behavior conformity for grid computing is an assurance that ad hoc related principals (users, platforms or instruments) forming a grid VO must each act in conformity with the rules for the VO constitution. We apply trusted computing technologies to achieve two levels of virtualization: resource virtualization and platform virtualization. The former is about behavior conformity in a grid VO and the latter, that in an operating system. With these two levels of virtualization working together it is possible to build a grid of truly unbounded scale by VO including servers from commercial organizations.     

A taxonomy of market‐based resource management systems for utility‐driven cluster computing

In utility‐driven cluster computing, cluster Resource Management Systems (RMSs) need to know the specific needs of different users in order to allocate resources according to their needs. This in turn is vital to achieve service‐oriented Grid computing that harnesses resources distributed worldwide based on users' objectives. Recently, numerous market‐based RMSs have been proposed to make use of real‐world market concepts and behavior to assign resources to users for various computing platforms. The aim of this paper is to develop a taxonomy that characterizes and classifies how market‐based RMSs can support utility‐driven cluster computing in practice. The taxonomy is then mapped to existing market‐based RMSs designed for both cluster and other computing platforms to survey current research developments and identify outstanding issues. Copyright © 2006 John Wiley & Sons, Ltd.     

基于改进GA的云计算任务调度算法

云计算通常需要处理大量的计算任务,任务调度策略在决定云计算效率方面起着关键作用。如何合理地分配计算资源,有效地调度任务运行,使所有任务运行完成所需的时间较短、成本较小是个重要的问题。提出一种考虑时间-成本约束的遗传算法(TCGA),通过此算法调度产生的结果不仅能使任务完成所需的时间较短,而且成本较小。通过实验,将TCGA与考虑时间约束的遗传算法(TGA)、考虑成本约束的遗传算法(CGA)进行比较,实验结果表明,该算法是云计算中一种有效的任务调度算法。     

ARMCO: Advanced topics in resource management for ubiquitous cloud computing: An adaptive approach

Cloud Computing can be seen as one of the latest major evolution in computing offering unlimited possibility to use ICT in various domains: business, smart cities, medicine, environmental computing, mobile systems, design and implementation of cyber-infrastructures. The recent expansion of Cloud Systems has led to adapting resource management solutions for large number of wide distributed and heterogeneous datacenters. The adaptive methods used in this context are oriented on: self-stabilizing, self-organizing and autonomic systems; dynamic, adaptive and machine learning based distributed algorithms; fault tolerance, reliability, availability of distributed systems. The pay-per-use economic model of Cloud Computing comes with a new challenge: maximizing the profit for service providers, minimizing the total cost for customers and being friendly with the environment.This special issue presents advances in virtual machine assignment and placement, multi-objective and multi-constraints job scheduling, resource management in federated Clouds and in heterogeneous environments, dynamic topology for data distribution, workflow performance improvement, energy efficiency techniques and assurance of Service Level Agreements.     

云计算环境下基于改进遗传算法的任务调度算法

在云计算中面对的用户群是庞大的,要处理的任务量与数据量也是十分巨大的。如何对任务进行高效的调度成为云计算中所要解决的重要问题。针对云计算的编程模型框架,提出了一种具有双适应度的遗传算法（DFGA）,通过此算法不但能找到总任务完成时间较短的调度结果,而且此调度结果的任务平均完成时间也较短。通过仿真实验将此算法与自适应遗传算法（AGA）进行比较,实验结果表明,此算法优于自适应遗传算法,是一种云计算环境下有效的任务调度算法。     

Time-bounded distributed QoS-aware service configuration in heterogeneous cooperative environments

The scarcity and diversity of resources among the devices of heterogeneous computing environments may affect their ability to execute services within the users’ requested Quality of Service levels, particularly in open real-time environments where the characteristics of the computational load cannot always be predicted in advance but, nevertheless, response to events still has to be provided within precise timing constraints in order to guarantee a desired level of performance.