自适应重配置软件系统的运行时监控方法研究

运行时监控技术作为实现自适应软件的一个重要研究内容,现已成为当前很多软件工程方法中用来提高软件产品可信性的一个重要设计原则。针对现有的很多软件监控方法常常将系统的监控逻辑与业务功能逻辑混杂在一起的问题,提出了一个需求模型驱动的、自适应重配置软件的运行时监控方法。以软件系统的目标模型及属性规约为基础,介绍了如何构建系统的监控模型、生成和编织监控代码,以及进行运行时诊断分析和自适应重配置调整。该方法通过采用独立于应用程序的外部单元来实现对运行时系统的监控、诊断和自适应重配置处理。这更利于系统的维护和管理,也更符合软件复用的思想。     

一种普适计算环境下自适应中间件

普适计算环境固有的内在复杂性对当前的基础软件提出了新的挑战,迫切需要一种具有感知和自适应能力的中间件.提出了一个由接口、框架和情境元模型组成的自适应中间件,给出了在CAR构件平台上的设计与实现.为获取构件信息和对外提供服务,接口元模型支持同步和异步接口.情境元模型在构件对象内建模情境信息,计算实体间以基于异步事件通知方式交互.框架元模型分类和管理构件,随着运行时计算环境的变化动态改变中间件的结构和行为.软件实体感知环境的变化,实体间以松耦合的方式交互,动态改变自身的结构和行为,满足普适计算环境下的动态自适应需求.     

自适应对象模型体系结构研究*

创建动态的、可配置和具有自适应能力的应用系统一直是软件开发人员追求的目标,自适应对象模型开发是一种有效的解决方法。自适应对象模型具有描述驱动、最终用户编程和运行时解释执行等特点,其体系结构由元模型、模型引擎和支撑工具组成。首先详细分析了元模型的六个模式,并讨论了模型引擎的原理和支撑工具,分析对开发带来的影响和阐述实现中的问题,最后提出了应用自适应对象模型方法开发的新的应用软件平台——应用件。     

高速列车自适应制动控制

针对传统控制方法难以保证高速列车在复杂多变的运行条件下的速度跟踪和安全、平稳停车问题,提出了一种基于间接模型参考自适应的高速列车制动控制策略.通过分析列车制动系统的工作流程,建立了具有未知参数的列车制动系统模型,并采用间接模型参考自适应控制(MRAC)方法设计了高速列车自适应制动控制策略,实现了对给定速度曲线的渐进跟踪.为了验证上述方法的有效性,以CRH380AL型动车组作为研究对象,利用MATLAB软件进行数值仿真.仿真结果表明,即使存在未知系统参数,基于间接模型参考自适应的高速列车制动控制策略仍然可以实现列车在制动过程中对目标速度的渐近跟踪,进而保证列车的安全可靠运行.     

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。     

基于验证的自适应系统决策:一种模型驱动的方法

自适应系统需要根据运行时上下文和自身的变化进行其行为的调节.为实现自主调节,自适应系统必须被赋予运行时监测上下文和自身变化,分析需求满足程度的变化,以及推理得到自适应决策的能力.这种在线决策的行为在满足功能需求的同时,还需要保证系统满足特定的非功能需求,如可靠性和性能等.本文提出了一种基于验证的自适应系统优化决策方法,以保证非功能需求的满足.该方法在识别可调节目标以建模自适应机制的同时,将系统的目标模型映射为相应的行为模型,用标签转移系统表示;以可靠性需求为例,用标记目标模型规约任务的可靠性;然后将系统行为模型和可靠性规约整合为带可变状态的离散时间马尔可夫链,将候选自适应配置描述为不同可变状态间的组合;最终通过相关需求的在线验证,使系统找到关于某类上下文的最优决策配置.本文通过一个移动信息系统的案例展示了该方法的可行性和有效性.     

一种基于策略的软件自适应框架

普适计算成为一种新的应用模式,普适计算运行环境的复杂性对软件自适应提出了新的挑战.提出一种基于策略的软件自适应框架,该框架采用基于上下文的自适应策略模型及上下文驱动的事件机制实现软件运行的自适应能力,该框架屏蔽了软件运行环境的复杂性,具有较强的可扩展性.     

高速动车组数据驱动无模型自适应控制方法

针对动车组的速度跟踪控制问题,同时考虑到现有基于模型的控制方法对系统动力学模型的依赖性,以及传统无模型自适应控制时变参数估计算法的复杂性,将改进的多输入多输出(Multiple-input multiple-output, MIMO)偏格式动态线性化无模型自适应控制(Partial form dynamic linearization-improved model-free adaptive control, PFDL-iMFAC)方法引入到动车组自动驾驶系统中.该控制方法在无模型自适应控制的基础上,考虑滑动时间窗口,增加了可调自由度和设计灵活性,并在输入准则函数中加上对能量函数的惩罚项,减少能量损耗,为动车组的跟踪精度和节能运行提供了一种优化的方法,在满足动车组速度跟踪效果好的前提下实现节能运行.最后以CRH380A动车组为对象进行仿真实验,通过与传统无模型自适应控制对比:所提出的控制算法各动力单元速度跟踪误差在±0.2 km/h以内,加速度在±0.65 m/s2以内且变化平稳,比传统无模型自适应控制方法节约9.86%的能量.     

基于机动转弯目标的自适应交互式多模型算法

在车辆转弯性能优化控制问题的研究中,针对转弯机动目标跟踪,由于控制模型为单模型,造成跟踪性能差.为解决上述问题,设计了一种利用自适应网格方法对模型集合进行自适应计算的方法.利用自适应网格,通过设定初始粗略网格,将转弯模型的转弯速率作为网格值进行自适应的调整,以期能够符合目标当前时刻的运动状态.然后与交互式多模型算法相结合,对模型进行滤波计算,以达到跟踪目标的目的.最后,通过仿真比较自适应网格交互式多模型算法与三种常规交互式多模型算法的跟踪效果,验证了算法的优越性,证明改进算法跟踪精度高、速度快,能够接近理想的模型设计.     

Models@ run.time

Runtime adaptation mechanisms that leverage software models extend the applicability of model-driven engineering techniques to the runtime environment. Contemporary mission-critical software systems are often expected to safely adapt to changes in their execution environment. Given the critical roles these systems play, it is often inconvenient to take them offline to adapt their functionality. Consequently, these systems are required, when feasible, to adapt their behavior at runtime with little or no human intervention. A promising approach to managing complexity in runtime environments is to develop adaptation mechanisms that leverage software models, referred to as models@run. time. Work on models@run.time seeks to extend the applicability of models produced in model-driven engineering (MDE) approaches to the runtime environment. Models@run. time is a causally connected self-representation of the associated system that emphasizes the structure, behavior, or goals of the system from a problem space perspective.     

An event-based approach for formally verifying runtime adaptive real-time systems

Real-time and embedded systems are required to adapt their behavior and structure to runtime unpredicted changes in order to maintain their feasibility and usefulness. These systems are generally more difficult to specify and verify owning to their execution complexity. Hence, ensuring the high-level design and the early verification of system adaptation at runtime is very crucial. However, existing runtime model-based approaches for adaptive real-time and embedded systems suffer from shortcoming linked to efficiently and correctly managing the adaptive system behavior, especially that a formal verification is not allowed by modeling languages such as UML and MARTE profile. Moreover, reasoning about the correctness and the precision of high-level models is a complex task without the appropriate tool support. In this work, we propose an MDE-based framework for the specification and the verification of runtime adaptive real-time and embedded systems. Our approach stands for Event-B method to formally verify resources behavior and real-time constraints. In fact, thanks to MDE M2T transformations, our proposal translates runtime models into Event-B specifications to ensure the correctness of runtime adaptive system properties, temporal constrains and nonfunctional properties using Rodin platform. A flood prediction system case study is adopted for the validation of our proposal.

     

服务化软件系统的运行时资源动态分配方法

Web软件是一种典型的基于Internet的软件形态,它自身的特点决定了其运行时的可靠保障以及质量优化都需要系统具有一定的自适应能力。对于服务化的Web软件系统而言,各服务之间的动态资源分配是实现运行时自适应的重要基础。针对这一问题,构建了一个基于Web的在线购物系统,并在此基础上对基于Web服务资源动态分配及负载均衡的Web软件系统运行时自适应技术进行了研究。对Web软件系统的运行时自适应管理问题进行了分析,提出并实现了资源动态分配及负载均衡方法,在此基础上针对在线购物系统进行了有效性验证。实验结果表明,优化的资源动态分配及负载均衡方法可以有效地提高Web软件系统的运行质量。     

A self-learning approach for validation of runtime adaptation in service-oriented systems

Ensuring that service-oriented systems can adapt quickly and effectively to changes in service quality, business needs and their runtime environment is an increasingly important research problem. However, while considerable research has focused on developing runtime adaptation frameworks for service-oriented systems, there has been little work on assessing how effective the adaptations are. Effective adaptation ensures the system remains relevant in a changing environment and is an accurate reflection of user expectations. One way to address the problem is through validation. Validation allows us to assess how well a recommended adaptation addresses the concerns for which the system is reconfigured and provides us with insights into the nature of problems for which different adaptations are suited. However, the dynamic nature of runtime adaptation and the changeable contexts in which service-oriented systems operate make it difficult to specify appropriate validation mechanisms in advance. This paper describes a novel consumer-centered approach that uses machine learning to continuously validate and refine runtime adaptation in service-oriented systems, through model-based clustering and deep learning.     

自适应策略描述语言编译器的设计与实现

复杂自适应系统中的软件实体既需要完成业务功能,又需要不断地感知环境,并根据环境的变化调整自己的结构和行为来适应环境,完成自适应功能。当前自适应系统的开发存在将自适应逻辑和业务逻辑相互缠绕的问题,使得自适应系统的开发和维护变得极为复杂和困难。本文将自适应系统中的自主运行单元抽象为自适应Agent,将自适应Agent的业务逻辑和自适应逻辑相分离,提出了表述Agent如何适应环境变化的自适应策略描述语言SADL。为了将自适应策略编译成可执行的程序单元,本文设计并实现了SADL编译器。通过案例分析阐述了如何定义自适应策略,并展示了编译结果,验证了方法的有效性。     

Systematic literature review of the objectives,techniques, kinds,and architectures of models at runtime

In the context of software development, models provide an abstract representation of a software system or a part of it. In the software development process, they are primarily used for documentation and communication purposes in analysis, design, and implementation activities. Model-Driven Engineering (MDE) further increases the importance of models, as in MDE models are not only used for documentation and communication, but as central artefacts of the software development process. Various recent research approaches take the idea of using models as central artefacts one step further by using models at runtime to cope with dynamic aspects of ever-changing software and its environment. In this article, we analyze the usage of models at runtime in the existing research literature using the Systematic Literature Review (SLR) research method. The main goals of our SLR are building a common classification and surveying the existing approaches in terms of objectives, techniques, architectures, and kinds of models used in these approaches. The contribution of this article is to provide an overview and classification of current research approaches using models at runtime and to identify research areas not covered by models at runtime so far.     

Achieving dynamic adaptation via management and interpretation of runtime models

In this article, we present a generic model-centric approach for realizing fine-grained dynamic adaptation in software systems by managing and interpreting graph-based models of software at runtime. We implemented this approach as the Graph-based Runtime Adaptation Framework (GRAF), which is particularly tailored to facilitate and simplify the process of evolving and adapting current software towards runtime adaptivity. As a proof of concept, we present case study results that show how to achieve runtime adaptivity with GRAF and sketch the framework's capabilities for facilitating the evolution of real-world applications towards self-adaptive software. The case studies also provide some details of the GRAF implementation and examine the usability and performance of the approach.     

An overview of Dynamic Software Product Line architectures and techniques: Observations from research and industry

Over the last two decades, software product lines have been used successfully in industry for building families of systems of related products, maximizing reuse, and exploiting their variable and configurable options. In a changing world, modern software demands more and more adaptive features, many of them performed dynamically, and the requirements on the software architecture to support adaptation capabilities of systems are increasing in importance. Today, many embedded system families and application domains such as ecosystems, service-based applications, and self-adaptive systems demand runtime capabilities for flexible adaptation, reconfiguration, and post-deployment activities. However, as traditional software product line architectures fail to provide mechanisms for runtime adaptation and behavior of products, there is a shift toward designing more dynamic software architectures and building more adaptable software able to handle autonomous decision-making, according to varying conditions. Recent development approaches such as Dynamic Software Product Lines (DSPLs) attempt to face the challenges of the dynamic conditions of such systems but the state of these solution architectures is still immature. In order to provide a more comprehensive treatment of DSPL models and their solution architectures, in this research work we provide an overview of the state of the art and current techniques that, partially, attempt to face the many challenges of runtime variability mechanisms in the context of Dynamic Software Product Lines. We also provide an integrated view of the challenges and solutions that are necessary to support runtime variability mechanisms in DSPL models and software architectures.     

Modeling run-time adaptation at the system architecture level in dynamic service-oriented environments

Today, software systems are more and more executed in dynamic, virtualized environments. These environments host diverse applications of different parties, sharing the underlying resources. The goal of this resource sharing is to utilize resources efficiently while ensuring that quality-of-service requirements are continuously satisfied. In such scenarios, complex adaptations to changes in the system environment are still largely performed manually by humans. Over the past decade, autonomic self-adaptation techniques aiming to minimize human intervention have become increasingly popular. However, given that adaptation processes are usually highly system-specific, it is a challenge to abstract from system details, enabling the reuse of adaptation strategies. In this paper, we present S/T/A, a modeling language to describe system adaptation processes at the system architecture level in a generic, human-understandable and reusable way. We apply our approach to multiple different realistic contexts (dynamic resource allocation, run-time adaptation planning, etc.). The results show how a holistic model-based approach can close the gap between complex manual adaptations and their autonomous execution.     

Model-based testing for software safety: a systematic mapping study

Testing safety-critical systems is crucial since a failure or malfunction may result in death or serious injuries to people, equipment, or environment. An important challenge in testing is the derivation of test cases that can identify the potential faults. Model-based testing adopts models of a system under test and/or its environment to derive test artifacts. This paper aims to provide a systematic mapping study to identify, analyze, and describe the state-of-the-art advances in model-based testing for software safety. The systematic mapping study is conducted as a multi-phase study selection process using the published literature in major software engineering journals and conference proceedings. We reviewed 751 papers and 36 of them have been selected as primary studies to answer our research questions. Based on the analysis of the data extraction process, we discuss the primary trends and approaches and present the identified obstacles. This study shows that model-based testing can provide important benefits for software safety testing. Several solution directions have been identified, but further research is critical for reliable model-based testing approach for safety.