面向用户的普适计算系统可用性度量模型

普适计算模式追求随时随地的计算境界,对相关可用性管理提出了很高的要求。现有研究大多基于定性的角度研究普适计算系统的可用性问题,本文尝试给出定量的可用性度量模型和分析。普适计算系统可用性具有典型的面向用户特点,为刻画用户行为需要引入具有非指数分布性质的模型状态,并且需要考虑用户态与系统态关联分析时的复杂情况。传统的连续时间马尔可夫链和半马尔可夫过程都不能很好地对以上情况给与分析。鉴于此,本文提出了一种基于马尔可夫重生过程(MRGP)的普适计算系统可用性度量方法。首先建立具有双扩展特点的用户模型,基于此构建了普适计算系统可用性度量的MRGP模型,对可用性进行了随机过程分析,给出了对应瞬态和稳态行为的度量方法,结合普适计算模式的特点定义了普适计算系统请求可用性度量。最后进行了数值分析,评价了用户和系统参数等因素对普适计算系统可用性度量的影响。     

普适计算系统可用性度量

普适计算系统可用性度量具有典型的面向用户特点,为刻画用户行为需要引入具有非指数分布性质的用户状态,并且需要考虑用户态与系统态关联分析时的复杂情况。传统的连续时间马尔科夫链和半马尔科夫过程都不能很好地对以上情况给予分析。鉴于此,文章基于马尔科夫重生过程(MRGP)提出了普适计算系统的可用性度量方法。首先建立具有普适计算模式的用户模型,基于此构建了普适计算系统可用性度量的MRGP模型,对可用性进行了随机过程分析,给出了对应瞬态和稳态行为的度量方法,结合普适计算模式的特点定义了普适计算系统请求可用性度量。最后进行了数值分析,评价了普适计算系统可用性在MTTF和MTTR下的变化情况,据此导出来构建高可用普适计算系统需要考虑一些原则,并分析了用户和系统参数等因素对普适计算系统可用性度量的影响。     

计算网格应用可用性的度量模型

计算网格是很容易变化的不可靠的计算环境，因此如何保证应用的可用性成为构建网格系统的关键问题之一，而首先要解决的问题是如何对计算网格的可用性进行度量，分析了计算网格可用性的特征，提出从应用的角度度量计算网格可用性的方法，并设计了一种度量计算网格中应用可用性的模型，在这一模型中，应用的可用性用一个串行RBD来描述，每个网格结点上运行的应用的任务的可用性用概率模型描述，以此模型为基础，首先分析了在计算网格中影响应用可用性的关键因素，之后讨论了该度量模型在构建计算网格高可用服务体系结构中的应用。     

基于Markov过程的IaaS系统可用性建模与分析方法

针对现有基础设施即服务（IaaS）可用性模型难以计算存在多个可用物理机器（PM）概率的问题，提出一种基于Markov过程的IaaS可用性分析方法。首先，将计算资源划分为hot PM、warm PM和cold PM三类；然后，结合资源分配过程的相应阶段对可用性影响进行建模，分别生成对应的三种分配子模型，子模型之间通过不同种类计算资源的转换关系相互协作，构建系统整体模型；其次，基于Markov过程建立方程组以对可用性模型进行求解；最后，结合实例对分析模型进行验证，并对PM变迁速率等关键影响因素进行了分析。实验结果表明，增加PM尤其是cold PM的数量有助于提升IaaS的可用性。所提方法可以用于评估IaaS存在一个或多个可用PM的概率。     

基于Markov过程的IaaS系统可用性建模与分析方法

针对现有基础设施即服务（IaaS）可用性模型难以计算存在多个可用物理机器（PM）概率的问题，提出一种基于Markov过程的IaaS可用性分析方法。首先，将计算资源划分为hot PM、warm PM和cold PM三类；然后，结合资源分配过程的相应阶段对可用性影响进行建模，分别生成对应的三种分配子模型，子模型之间通过不同种类计算资源的转换关系相互协作，构建系统整体模型；其次，基于Markov过程建立方程组以对可用性模型进行求解；最后，结合实例对分析模型进行验证，并对PM变迁速率等关键影响因素进行了分析。实验结果表明，增加PM尤其是cold PM的数量有助于提升IaaS的可用性。所提方法可以用于评估IaaS存在一个或多个可用PM的概率。     

一种Hadoop集群下的行为异常检测方法

随着分布式计算技术的发展,Hadoop成为大规模数据处理领域的典型代表,由于安全机制相对薄弱,缺少用户行为活动的监控,容易受到隐藏的安全威胁,如数据泄露等。结合主成分分析计算的特点,基于MapReduce对其做并行化处理,克服了传统主成分分析计算的缺点,提高了模型训练效率。提出了一种基于并行化主成分分析的异常行为检测方法,即比较当前用户的行为模式是否与历史行为模式相匹配作为判定用户行为异常与否的度量标准。实验表明该方法能够较好地发现用户的异常行为。     

并行计算机高可用性分析与设计

随着并行计算机系统规模的不断增大，系统的失效率呈线性增长。如何保证大规模并行系统能够提供持续不断的服务，即提高系统的可用性，达到高可用的目标，已成为并行系统设计的重要方面。系统级容错的概念目前已经提出，但系统可用性的度量仍然需要深入研究。本文运用组合模型和马尔科夫过程模型，对系统可靠性和可用性进行了建模模和分析，推导了基于马尔科夫过程的可用性度量公式，得出运用高可用技术可以提高系统的可用性。在此基础上，还给出了一个大规模并行计算机系统的高可用系统结构。     

基于PCTBTA任务模型的用户界面开发方法

为了适应普适计算环境中用户、设备、使用环境和开发平台的多样性,基于模型的方法被应用于用户界面开发过程中,试图在抽象层次上描述界面,通过模型转换,使其适用于不同的平台.然而,由于目前基于模型的用户界面开发方法（model-based user interface development,简称MBUID）中所采用任务模型的局限性,致使生成的界面难以满足动态环境下用户的可用性需求.提出一种基于任务模型的用户界面开发框架,旨在建模和生成有效、高效、令用户满意的用户界面.在可用性方面,为了准确描述普适计算环境中用户任务,提出一种基于感知控制理论的任务分析方法（perceptual-control-theory-based task analysis,简称PCTBTA）,将使用上下文信息引入到任务分析过程中,并且在较高的抽象层次上反映交互的内容,给可用性设计提供任务空间;在技术方面,为PCTBTA任务模型向界面模型的转换提供技术支持.最后,通过实例说明所提出方法的可行性,并通过与其他方法在可用性和性能方面的比较,表明该方法的有效性.     

用户行为模式挖掘问题的研究

在软件可用性测试中，分析用户行为模式是一个关键的问题。为解决具有序列长度长、以序列片断为支持度计算依据等特点的用户行为模式挖掘问题，提出了一种有效的基于前缀树的频繁事件序列扩展方法，给出了比特图索引表的构造、事件扩展、事务扩展以及支持度计算的算法。使频繁事件序列能够简单快速地被确定。     

智能影子(SmartShadow):一个普适计算模型

普适计算是一种全新的计算模式,目前对普适计算建模的研究较少.尝试以用户为中心将普适计算环境建模为一个“智能影子”模型.该模型建立在用户建模及普适服务抽象的基础上,通过用户BDP模型,将普适环境映射成一个以用户为中心的高度动态的移动的虚拟个人空间,也称“智能影子”,与物理世界的影子一样跟随用户,如影相随.其中,用户BDP模型根据信念(belief)推理出用户意图(desire),并对普适服务的计算过程进行规划(plan).普适服务用来抽象信息空间中的计算资源,可被用户BDP组织,完成用户的意图,组织的过程即为智能影子的映射过程.智能影子模型在逻辑上简单、自然,且可灵活处理普适计算空间的动态变化.另外,还实现了一个仿真原型系统,对模型的可行性进行了验证.     

基于感知控制的场景界面设计方法

在普适环境下,计算环境的复杂性使普通用户不易通过预定义的操作来控制系统的状态,致使用户界面设计不能正确反映系统 的特征。为此,提出一种基于感知控制的场景设计方法。该方法利用感知控制理论对使用场景进行分析,导出用户意图控制的对象及策 略,并在此基础上重新设计使用场景,从用户任务的角度出发,使所得到的界面满足用户感知控制的需要。通过案例分析证明该方法的有效性。     

Task-driven automated component deployment for ambient intelligence environments

This article presents a strategy for deploying component-based applications gradually in order to match the functionality of pervasive computing applications onto the current needs of the user. We establish this deployment strategy by linking component composition models with task models at design-time, from which a run-time deployment plan is deduced. Enhanced with a Markov model, this deployment plan is able to drive a component life cycle manager to anticipate future deployments. The result is a seamless integration of pervasive computing applications with the user’s tasks, guaranteeing the availability of the required functionality without wasting computing resources on components that are not currently needed.     

A distributed programs monitor for berkeley UNIX

Writing and debugging distributed programs can be difficult. When a program is working, it can be difficult to achieve reasonable execution performance. A major cause of these difficulties is a lack of tools for the programmer. We use a model of distributed computation and measurement to implement a program monitoring system for programs running on the Berkeley UNIX 4.2BSD operating system. The model of distributed computation describes the activities of the processes within a distributed program in terms of computation (internal events) and communication (external events). The measurement model focuses on external events and separates the detection of external events, event record selection and data analysis. The implementation of the measurement tools involved changes to the Berkeley UNIX kernel, and the addition of daemon processes to allow the monitoring activity to take place across machine boundaries. A user interface has also been implemented.     

Dynamic Service Composition in Pervasive Computing

Service-oriented architectures (SOAs) promise to provide transparency to resource access by exposing the resources available as services. SOAs have been employed within pervasive computing systems to provide essential support to user tasks by creating services representing the available resources. The mechanism of combining two or more basic services into a possibly complex service is known as service composition. Existing solutions to service composition employ a template-matching approach, where the user needs are expressed as a request template, and through composition, a system would identify services to populate the entities within the request template. However, with the dynamism involved in pervasive environments, the user needs have to be met by exploiting available resources, even when an exact match does not exist. In this paper, we present a novel service composition mechanism for pervasive computing. We employ the service-oriented middleware platform called pervasive information communities organization (PICO) to model and represent resources as services. The proposed service composition mechanism models services as directed attributed graphs, maintains a repository of service graphs, and dynamically combines multiple basic services into complex services. Further, we present a hierarchical overlay structure created among the devices to exploit the resource unevenness, resulting in the capability of providing essential service-related support to resource-poor devices. Results of extensive simulation studies are presented to illustrate the suitability of the proposed mechanism in meeting the challenges of pervasive computing user mobility, heterogeneity, and the uncertain nature of involved resources.     

Improving memory-based collaborative filtering via similarity updating and prediction modulation

Memory-based collaborative filtering (CF) makes recommendations based on a collection of user preferences for items. The idea underlying this approach is that the interests of an active user will more likely coincide with those of users who share similar preferences to the active user. Hence, the choice and computation of a similarity measure between users is critical to rating items. This work proposes a similarity update method that uses an iterative message passing procedure. Additionally, this work deals with a drawback of using the popular mean absolute error (MAE) for performance evaluation, namely that ignores ratings distribution. A novel modulation method and an accuracy metric are presented in order to minimize the predictive accuracy error and to evenly distribute predicted ratings over true rating scales. Preliminary results show that the proposed similarity update and prediction modulation techniques significantly improve the predicted rankings.     

User feature-aware trustworthiness measurement of cloud services via evidence synthesis for potential users

Cloud computing can provide elastic and dynamic resources on demand, which facilitates service providers to make profits resulting from the long tail effect. It becomes vitally important to ensure that cloud services can be acceptable to more potential users. However, it is challenging for potential users to discover the trustworthy cloud services due to the deficiency of usage experiences and the information overload of QoE (quality of experience) evaluations from consumers. This paper presents a user feature-aware trustworthiness measurement approach for potential users. In this approach, the influence factors of QoE are systematically analyzed based on the user feature model and the quantitative computation methods are designed to measure the user feature similarity. In addition, employing FAHP (fuzzy analytic hierarchy process) method identifies the user feature community. To enhance the accuracy of trustworthiness measurement, the false evidences in QoE evaluations are iteratively filtered out with dynamic mean distance threshold. Finally, the trustworthiness of service is measured via evidence synthesis combining user feature similarity. The experiments show that this approach is effective to improve the quality of trustworthiness measurement, which is helpful to solve information overload problem and cold start problem of trusted service recommendation for potential users.     

Guest Editors' Introduction: Energy Harvesting and Conservation

Pervasive computing aims to integrate computation into our daily work practice to enhance our activities without being noticed. In other words, computing becomes truly invisible. Yet at the heart of every pervasive computing system are electronic components that consume energy. Managing the energy needs of mobile systems, or systems for which reliable power isn’t guaranteed, can be a significant distraction for users. How can we minimize user involvement in the energy management process to make pervasive computing devices more pervasive?