面向环境自适应组合服务系统的环境变化识别

环境自适应的组合服务系统是未来软件系统的一个发展方向,不同于普通组合服务系统之处在于这种系统对系统运行的网络环境进行监测,并根据监测结果对组合服务进行合适的调整以保证软件系统的服务质量.由于通常的类别匹配式环境感知策略不满足系统中服务QoS连续性的需求,本文经过分析环境自适应的组合服务系统的运行原理,提出一个基于动态QoS计算的环境变化识别方法,实现了从环境监测到输出变化事件的系统的环境感知功能,给出了环境监测及变化识别策略,设计了整体的变化识别算法以及其中具体的变化识别阈值计算算法,并通过实验结果表明了变化识别方法可以良好并高效地完成系统的环境感知需求.     

一种基于智能体技术的软件自适应动态演化机制

针对分布式软件系统在动态演化中面临的原有软件单元难以重用、忽视软件内部运行状态引发的演化需求等问题,借助智能体(agent)具有的环境适应性、变化敏感性等特征,提出了一种基于智能体技术的软件自适应动态演化机制.通过将软件单元封装为Agent,并定义单元间的演化规则,使演化机制重用原有软件单元.通过一种基于数据推送的动态环境感知方法实现Agent间协作关系调整,同时满足来自内外部环境引发的动态演化需求.通过引入信息中介服务,实现了基于改进合同网的Agent协作策略,能够自适应地更替Agent,满足用户意愿变更引发的动态演化需求.依据演化机制在支撑环境中的运行情况及相关能力指标的分析,说明所提出的演化机制适用于动态复杂的分布式软件系统,是一种有效的软件自适应动态演化机制.     

基于事件关系保障识别质量的自适应分析方法

目前自适应软件正在为众多领域系统提供着对运行环境的适应能力.如何建立一种能够保障识别质量的自适应分析方法,使之可从运行环境中快速且准确地识别出异常事件,是确保自适应软件长期稳定运行所必须考虑的研究问题之一.当前运行环境的不确定性给该问题的攻关带来两方面挑战：其一,现有分析方法一般通过预先建立环境状态与事件之间的映射关系以识别事件.但在系统运行前,已无法仅凭经验确定环境状态并建立全面且正确的映射关系.仅依赖映射关系建立分析方法的设计思路已无法保障识别准确性.其二,不确定环境何时会发生何种事件已变得不可预期.如果采用现有设计思路,定期获取环境状态再进行事件识别,则无法保障识别效率.然而目前却缺乏应对这些紧迫挑战的相关工作,因此本文提出了一种基于事件关系保障识别质量的自适应分析方法（Self-adaptation Analysis method For recognition quality assurance using Event Relationships,SAFER）.SAFER采用序列模式挖掘算法、模糊故障树与贝叶斯网络等技术抽取并建模事件因果关系,并基于该类关系与映射关系通过贝叶斯网络的正向推理能力共同识别事件,与传统仅依赖映射关系的识别方法相比可保证识别准确性;基于贝叶斯网络的反向推理能力,确定易引发事件的精英感知对象,并动态调整获取精英感知对象状态数据的采样周期,以便于在事件发生后尽快获得相关环境状态,从而保障识别效率.实验结果表明,在自适应软件实际运行过程中,SAFER能实现对事件的识别并保障识别准确性与识别效率,为自适应软件稳定运行提供了有效支持.     

自适应系统软件传感器设计与实现

随着Internet的普及应用,越来越多的软件系统运行在开放的环境中,需要感知和适应环境的变化.如何支持这类复杂软件系统的开发和运行已经成为当前软件工程面临的一项重要挑战.针对自适应系统与其驻留环境的交互问题,将自适应系统中的软件实体抽象为自主Agent,提出了自主Agent感知环境变化的软件传感器及其与环境的动态关联思想,给出了软件传感器的设计和实现.不同于已有研究,将软件传感器视为一类特殊的软件Agent.最后通过案例分析展示了上述思想和技术的可行性和有效性.     

基于可拓学的自适应软件形式化方法

可拓学的核心是建立灵活变通地应对不确定变化和灵感涌现的适应性模型。讨论引入可拓理论去描述、分析和评价软件系统的自适应性质、范围和程度的可能性。用基元描述软件实体,将软件系统构造成基元网,利用拓展分析、可拓变换和优度评价等定性与定量相结合的方法揭示了自适应软件系统的动态性质,并形成了一种自适应软件形式化方法。     

基于Agent的自主构件模型设计与评估

为适应复杂环境和业务需求的变化,自适应软件开发要求为系统及其构成成分提供新的抽象和建模手段。自主构件能够感知并依据环境的变化自动地做出决策。采用自主构件开发复杂软件系统的主要目的是有效降低软件开发和维护的复杂性。在分析自适应软件实体功能和特征的基础上,提出一种基于智能体(Agent)的自主构件模型,该自主构件模型能动态感知环境的变化,合理封装自适应逻辑,依据策略规则进行决策,并根据目标导向,自动规划执行行为的序列。评估结果表明,该模型为建模与开发复杂自适应化软件提供了有效的底层支持。     

组件化服务与业务流程提取模型

为了使得客户业务需求和软件实现一致,软件开发团队力求找到正确的适合团队的软件开发过程.为达到软件变化尽快适应需求变化,开发团队需要一定的工具和分析方法支持.SOA的出现,为软件组织构建灵活应对需求变化的软件系统指明了方向.通过分析SOA的特点和面向服务分析与设计方法论,提出了一个基于SOA的组件化服务与业务流程提取模型,该模型可以作为SOA软件系统服务分析与提取的方法.     

基于Agent和带权关系网的软件体系结构自适应

作为软件的蓝图,描述整个系统的结构和行为模型的软件体系结构在软件自适应中起着重要作用,具有适应性特征的Agent为构造自适应软件系统提供了独特的优势.针对现有基于Agent的软件系统在体系结构和动态配置方面的不足,提出了一种基于带权关系网模型,利用Agent对环境的监测和评估,以调整Agent间联系的权重来决策Agent间的合作对象的选择,从而使得基于Agent的软件系统在构件、连接件和配置方面具备适应性,为开发基于Agent的自适应软件系统提供借鉴和参考.     

基于反射机制的多Agent数据交互模型研究

在研究Agent理论的基础上,提出软件系统的智能性、灵活性主要表现在软件系统能够感知环境与需求的变化,根据变化来对自身业务流程做出相应的调整.重点在于结合反射技术,通过动态配置器的参与作为决策依据进行研究,建立基于反射机制的多Agent数据交互配置模型,在现有的多Agent合作和协调机制下,该模型为开放、异构、动态环境下多Agent数据交互提供灵活、自适应的解决方案,并将该模型应用于实际系统的设计中.     

面向非确定性的软件质量保障方法与技术专题前言

随着互联网、物联网、云计算等新计算平台、新应用模式、及智能化等新软件模式的广泛运用,软件系统内外各种来源的非确定性不断增强.从软件系统内部的不确定性看,并发程序是一类典型的非确定性软件系统.并发程序由于其随机性高的特点,容易导致并发缺陷且难以调试.从软件系统外部的不确定性看,软件所处的网络环境和所服务的用户需求变得更加动态多变,这就要求软件系统能够主动应对这些动态变化.具有自适应和持续演化能力的软件系统需要在环境和需求的自动感知与理解、适应行为的自主决策、以及适应行为的精准实施等环节处理各种不确定性,以保障系统能够持续稳定地提供服务.从软件构造途径的不确定性看,包含深度神经网络部件的数据驱动智能化软件系统是另一类非确定性软件系统,其非确定性来自于机器学习模型的归纳本质.此类系统日益应用于一些安全相关的领域,这就对其软件质量提出了更高的要求.本专题关注软件质量保障中非确定性问题所面临的挑战以及相关软件质量保障技术. 本专题采取自由投稿的方式,共收到24篇投稿.特约编辑邀请了近20位领域专家参与审稿,每篇稿件至少邀请2位专家进行评审,每篇稿件均经过至少两轮审稿.共计16篇稿件通过评审,并在中国软件大会上进行了报告,最终该16篇论文入选本专题.     

Designing an adaptive computer‐aided ambulance dispatch system with Zanshin: an experience report

We have been witnessing growing interest in systems that can adapt their behavior to deal with deviations between their performance and their requirements at run‐time. Such adaptive systems usually need to support some form of a feedback loop that monitors the system's output for problems and carries out adaptation actions when necessary. Being an important feature, adaptivity needs to be considered in early stages of development. Therefore, adopting a requirements engineering perspective, we have proposed an approach and a framework (both called Zanshin) for the engineering of adaptive systems based on a feedback loop architecture. As part of our framework's evaluation, we have applied the Zanshin approach to the design of an adaptive computer‐aided ambulance dispatch system, whose requirements were based on a well‐known case study from the literature. In this paper, we report on the application of Zanshin for the design of an adaptive computer‐aided ambulance dispatch system, presenting elements of the design, as well as the results from simulations of run‐time scenarios. Copyright © 2013 John Wiley & Sons, Ltd.     

基于Tropos~+需求模型的软件自适应方法

在模型驱动的软件自适应控制过程中,监测、分析、决策和执行等活动均基于共享的知识模型。为便于知识重用和运行时维护,常采用抽象级别较高的需求模型来表示知识。为建模软件的适应性需求,针对传统的Tropos及其扩展方法不能用于软件对异常事件适应性需求建模问题,对Tropos进行上下文和异常条件扩展,记为Tropos+。在此基础上,提出一种由Tropos+需求模型驱动的软件自适应方法,该方法能够用于软件运行环境和异常事件监测以及软件对环境变化和异常事件的自适应处理。最后通过一个案例说明了软件自适应过程。     

Feedback linearisation control of an induction machine augmented by single-hidden layer neural networks

We consider adaptive output feedback control methodology of highly uncertain nonlinear systems with both parametric uncertainties and unmodelled dynamics. The approach is also applicable to systems of unknown, but bounded dimension. However, the relative degree of the regulated output is assumed to be known. This new control strategy is proposed to address the tracking problem of an induction motor based on a modified field-oriented control method. The obtained controller is then augmented by an online neural network that serves as an approximator for the neglected dynamics and modelling errors. The network weight adaptation rule is derived from the Lyapunov stability analysis, that guarantees boundedness of all the error signals of the closed-loop system. Computer simulations of an output feedback controlled induction machine, augmented via single-hidden-layer neural networks, demonstrate the practical potential of the proposed control algorithm.     

Uncertainty handling in goal-driven self-optimization – Limiting the negative effect on adaptation

Goal-driven self-optimization through feedback loops has shown effectiveness in reducing oscillating utilities due to a large number of uncertain factors in the runtime environments. However, such self-optimization is less satisfactory when there contains uncertainty in the predefined requirements goal models, such as imprecise contributions and unknown quality preferences, or during the switches of goal solutions, such as lack of understanding about the time for the adaptation actions to take effect. In this paper, we propose to handle such uncertainty in goal-driven self-optimization without interrupting the services. Taking the monitored quality values as the feedback, and the estimated earned value as the global indicator of self-optimization, our approach dynamically updates the quantitative contributions from alternative functionalities to quality requirements, tunes the preferences of relevant quality requirements, and determines a proper timing delay for the last adaptation action to take effect. After applying these runtime measures to limit the negative effect of the uncertainty in goal models and their suggested switches, an experimental study on a real-life online shopping system shows the improvements over goal-driven self-optimization approaches without uncertainty handling.     

Towards a goal‐driven approach to action selection in self‐adaptive software

Self‐adaptive software is a closed‐loop system, since it continuously monitors its context (i.e. environment) and/or self (i.e. software entities) in order to adapt itself properly to changes. We believe that representing adaptation goals explicitly and tracing them at run‐time are helpful in decision making for adaptation. While goal‐driven models are used in requirements engineering, they have not been utilized systematically yet for run‐time adaptation. To address this research gap, this article focuses on the deciding process in self‐adaptive software, and proposes the Goal‐Action‐Attribute Model (GAAM). An action selection mechanism, based on cooperative decision making, is also proposed that uses GAAM to select the appropriate adaptation action(s). The emphasis is on building a light‐weight and scalable run‐time model which needs less design and tuning effort comparing with a typical rule‐based approach. The GAAM and action selection mechanism are evaluated using a set of experiments on a simulated multi‐tier enterprise application, and two sample ordinal and cardinal action preference lists. The evaluation is accomplished based on a systematic design of experiment and a detailed statistical analysis in order to investigate several research questions. The findings are promising, considering the obtained results, and other impacts of the approach on engineering self‐adaptive software. Although, one case study is not enough to generalize the findings, and the proposed mechanism does not always outperform a typical rule‐based approach, less effort, scalability, and flexibility of GAAM are remarkable. Copyright © 2011 John Wiley & Sons, Ltd.     

Robust Adaptive Fuzzy Control of Nonlinear Systems with Unknown and Time‐Varying Saturation

In this paper, a robust adaptive fuzzy control approach is proposed for a class of nonlinear systems in strict‐feedback form with the unknown time‐varying saturation input. To deal with the time‐varying saturation problem, a novel controller separation approach is proposed in the literature to separate the desired control signal from the practical constrained control input. Furthermore, an optimized adaptation method is applied to the dynamic surface control design to reduce the number of adaptive parameters. By utilizing the Lyapunov synthesis, the fuzzy logic system technique and the Nussbaum function technique, an adaptive fuzzy control algorithm is constructed to guarantee that all the signals in the closed‐loop control system remain semiglobally uniformly ultimately bounded, and the tracking error is driven to an adjustable neighborhood of the origin. Finally, some numerical examples are provided to validate the effectiveness of the proposed control scheme in the literature.     

Output-feedback MRAC of networked control systems with packet dropout

In this article, a technique of output-feedback model reference adaptive control for networked control systems is developed. The key issues of networked control systems such as channel bandwidth and data-packets dropout induced by the insertion of data networks in the feedback adaptive control loops are considered. The advantage of this article over earlier ones is that the combination of different aspects in networked control systems, output-feedback model reference control of systems with unknown parameters, and unknown data-packets dropout. Error models, adaptive laws, and stability analysis are derived in the case of uncertainty due to data-packets dropout. The applicability of the approach is demonstrated in a practical numerical example of a ship-steering adaptive system.     

Adaptive unit vector control of multivariable systems using monitoring functions

An adaptive sliding‐mode unit vector control approach based on monitoring functions to deal with disturbances of unknown bounds is proposed. An uncertain multivariable linear system is considered with a quite general class of nonsmooth disturbances. Global stabilization/tracking is demonstrated using either state or output feedback. The proposed adaptation method makes the control gain less conservative, becoming large enough when the disturbance grows and becoming smaller when it decreases, leading to reduced chattering effects. In contrast to previous methods, the new switching scheme is able to guarantee a prespecified transient time, maximum overshoot, and steady‐state error for multivariable uncertain plants. The proposed technique is applied to the trajectory tracking control of a surface vessel subjected to ocean currents, wind, and waves. Simulations are presented to show the performance of the new adaptation scheme in this adverse scenario of possibly growing, temporarily large, or vanishing exogenous disturbances.     

A new adaptive backstepping method for nonlinear control of turbine main steam valve

A new approach for nonlinear adaptive control of turbine main steam valve is developed. In comparison with the existing controller based on ＂classical＂ adaptive backstepping, this method does not follow the classical certaintyequivalence principle in the design of adaptive control law. We introduce this approach, for the first time, to power systems and present a novel parameter estimator and dynamic feedback controller for a single machine infinite bus （SMIB） system with steam valve control. This system contains unknown parameters such as reactance of transmission lines. Besides preserving useful nonlinearities and the real-time estimation of uncertain parameters, the proposed approach possesses better performances with respect to the response of the system and the speed of adaptation. The simulation results demonstrate that the proposed approach is better than the design based on ＂classical＂ adaptive backstepping in terms of properties of stability and parameter estimation, and recovers the performance of the ＂full-information＂ controller. Hence, the proposed method provides an alternative for engineers in applications.     

Neural networks-based adaptive control for a class of nonlinear bioprocesses

The paper studies the design and analysis of a neural adaptive control strategy for a class of square nonlinear bioprocesses with incompletely known and time-varying dynamics. In fact, an adaptive controller based on a dynamical neural network used as a model of the unknown plant is developed. The neural controller design is achieved by using an input–output feedback linearization technique. The adaptation laws of neural network weights are derived from a Lyapunov stability property of the closed-loop system. The convergence of the system tracking error to zero is guaranteed without the need of network weights convergence. The resulted control method is applied in a depollution control problem in the case of a wastewater treatment bioprocess, belonging to the square nonlinear class, for which kinetic dynamics are strongly nonlinear, time varying and not exactly known.