Platform-Centric Self-Awareness as a Key Enabler for Controlling Changes in CPS
Platform-Centric Self-Awareness as a Key Enabler for Controlling Changes in CPS
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DOI：
10.1109/jproc.2018.2858023
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发表时间：
2018-09
影响因子：
20.6
通讯作者：
Mischa Möstl;Johannes Schlatow;R. Ernst;N. Dutt;Ahmed Nassar;A. Rahmani;F. Kurdahi;Thomas Wild;Armin Sadighi;A. Herkersdorf
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文献类型：
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作者：
Mischa Möstl;Johannes Schlatow;R. Ernst;N. Dutt;Ahmed Nassar;A. Rahmani;F. Kurdahi;Thomas Wild;Armin Sadighi;A. Herkersdorf
未来的网络物理系统将在硬件平台上托管大量共存的分布式应用程序，这些硬件平台上有数千到数百万个联网组件通过开放网络进行通信。这些应用和网络会不断变化。目前设计过程和实地作业的分离将被适应、内场整合和更新的终身设计过程所取代。持续的变化和发展、应用程序干扰、环境动态和不确定性导致了复杂的影响，必须加以控制才能满足不断增长的平台和应用程序需求。已提出以自我意识和自我配置为基础的自我适应，作为这种持续的实地进程的基础。需要研究来开发自动化的现场设计方法和工具，并提供所需的安全性、可用性和安全性保证。本文展示了网络物理系统的体系结构、方法和工具中自我意识的两个互补用例。第一个用例侧重于自我感知车辆平台中的安全性和可用性保证。它结合了合同机制、基于工具的自我分析和自我配置。提出了自主执行这些工具和机制的软件体系结构和运行时环境，包括针对故障和安全威胁的自我保护方面。第二个用例解决了网络化MPSoC中的可变性和长期演变，集成了监视、监测和持续适应的硬件和软件机制。这种方法类似于制造工厂的物流和运营原则，从而产生了信息处理工厂的比喻术语，它依赖于增量更改和反馈控制。这两种用例都由更大的研究小组进行调查。尽管它们的方法不同，但这两个用例都面临着相似的设计和设计自动化挑战，这些挑战将在最后进行总结。我们将争辩说，看似无关的研究挑战，如在机器学习和安全方面，也可以从自我意识系统的方法和卓越的建模能力中受益。
Future cyber–physical systems will host a large number of coexisting distributed applications on hardware platforms with thousands to millions of networked components communicating over open networks. These applications and networks are subject to continuous change. The current separation of design process and operation in the field will be superseded by a life-long design process of adaptation, infield integration, and update. Continuous change and evolution, application interference, environment dynamics and uncertainty lead to complex effects which must be controlled to serve a growing set of platform and application needs. Self-adaptation based on self-awareness and self-configuration has been proposed as a basis for such a continuous in-field process. Research is needed to develop automated in-field design methods and tools with the required safety, availability, and security guarantees. The paper shows two complementary use cases of self-awareness in architectures, methods, and tools for cyber–physical systems. The first use case focuses on safety and availability guarantees in self-aware vehicle platforms. It combines contracting mechanisms, tool based self-analysis and self-configuration. A software architecture and a runtime environment executing these tools and mechanisms autonomously are presented including aspects of self-protection against failures and security threats. The second use case addresses variability and long term evolution in networked MPSoC integrating hardware and software mechanisms of surveillance, monitoring, and continuous adaptation. The approach resembles the logistics and operation principles of manufacturing plants which gave rise to the metaphoric term of an Information Processing Factory that relies on incremental changes and feedback control. Both use cases are investigated by larger research groups. Despite their different approaches, both use cases face similar design and design automation challenges which will be summarized in the end. We will argue that seemingly unrelated research challenges, such as in machine learning and security, could also profit from the methods and superior modeling capabilities of self-aware systems.