CSR-SMA: Engineering Reliability Into Hybrid Systems via Rich Design Models

计算机系统正在变得无处不在，并且在规模、复杂性、分布性和异构性方面快速增长。由于这些原因，对工程师来说，对各种系统的感兴趣的属性进行定量推理并控制其设计、开发和部署成本变得越来越重要。在理想情况下，重要的系统特性，如性能和可靠性，应在系统设计时进行评估，然后才将大量时间和成本投入到项目中。然而，在早期设计阶段做出有用的(定量)预测充其量是困难的，因为许多相关因素之间的相互作用，例如软件组件的复杂属性、固件(硬件、操作系统、设备驱动程序)对软件的潜在影响以及潜在冲突的期望系统属性。即使是这些问题的一小部分也是具有挑战性的，并将导致复杂系统工程中最先进的技术的显著进步。因此，该项目建议将重点放在针对一个关键属性-可靠性-的体系结构的设计时评估上。这里，可靠性被定义为系统将在指定的设计极限下执行其预期功能的概率。拟议的方法将使工程师能够构建系统的多方面、分层模型，并以增量、可扩展的方式评估其可靠性。尽管存在几种软件可靠性技术，但它们是不够的。为了解决这些不足，该项目将开发一种将软件体系结构模型(系统设计者很好地理解)与扩展隐马尔可夫模型(其允许我们对早期设计阶段存在的大量不确定性进行推理)相结合的技术，并将利用固件的相关属性来增强该方法，以支持更完整和更有意义的可靠性模型。该项目将评估沿着两个感兴趣的度量开发的方法的结果：可处理性(旨在解决真实、复杂系统中存在的可扩展性问题)和敏感性(旨在解决在设计时存在的大量不确定性下研究人员预测的可信度问题)，并将结果应用于移动机器人领域的实际问题，代表许多复杂、分布式和嵌入式系统的问题域。

Computer systems are becoming pervasive as well as growing rapidly in scale, complexity, distribution, and heterogeneity. For these reasons, it is becoming increasingly important for engineers to reason quantitatively about the various systems' properties of interest and curb their design, development, and deployment costs. In an ideal situation important system characteristics, such as performance and reliability, would be assessed at system design time, before significant time and cost have been devoted to a project. However, making useful (quantitative) predictions in early design stages is difficult at best, due ti the interplay between many relevant factors, such as complex properties of software components, the potential effects on software of the firmware (hardware, OS, device drivers), as well as the potentially conflicting desired system attributes.Attacking even a small subset of these problems is challenging enough and would result in significant advances to the state of the art in complex systems engineering. Hence, this project proposes to focus efforts on design-time evaluation of architectures with respect to one key attribute - reliability. Here, reliability is defined as the probability that the system will perform its intended functionality under specified design limits. The proposed approach will enable an engineer to build a multi-faceted, hierarchical model of a system and assess its reliability in an incremental, scalable fashion. Although several software reliability techniques exist, they are insufficient. To address these deficiencies, the project will develop a technique that will couple software architectural models (well understood by system designers) with augmented Hidden Markov Models (which allow us to reason about numerous uncertainties existing in early design phases), and will augment this methodology with the relevant attributes of the firmware in support of more complete and meaningful reliability models.The project will evaluate the results the methods developed along two measures of interest: tractability (intended to address scalability issues existing in real, complex systems) and sensitivity (intended to address issues of confidence in the researchers predictions under numerous uncertainties existing at design time) and will apply the results to real problems from the domain of mobile robotics, a problem domain that is representative of many complex, distributed, and embedded systems.