System Identification I

This review covers research on the topic of mixed criticality systems that has been published since Vestal’s 2007 paper. It covers the period up to and including December 2015. The review is organised into the following topics: introduction and motivation, models, single processor analysis (including job-based, hard and soft tasks, fixed priority and EDF scheduling, shared resources and static and synchronous scheduling), multiprocessor analysis, related topics, realistic models, formal treatments, and systems issues. An appendix lists funded projects in the area of mixed criticality.

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Mixed Criticality Systems - A Review

This survey covers research into mixed criticality systems that has been published since Vestal’s seminal paper in 2007, up until the end of 2016. The survey is organised along the lines of the major research areas within this topic. These include single processor analysis (including fixed priority and Earliest Deadline First (EDF) scheduling, shared resources, and static and synchronous scheduling), multiprocessor analysis, realistic models, and systems issues. The survey also explores the relationship between research into mixed criticality systems and other topics such as hard and soft time constraints, fault tolerant scheduling, hierarchical scheduling, cyber physical systems, probabilistic real-time systems, and industrial safety standards.

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A Survey of Research into Mixed Criticality Systems

Mixed criticality systems have recently received much attention both in research and in industrial design practice. However, industrial practice and much of the research community seem to follow different objectives and research directions. We will review the status of mixed criticality systems design and compare the open challenges with the current research trends.

Mixed Criticality Systems—A History of Misconceptions?

Adapting an agile manufacturing concept to the reference architecture model industry 4.0: A survey and case study

When modelling software components for timing analysis, we typically encounter functional chains of tasks that lead to precedence relations. As these task chains represent a functionally-dependent sequence of operations, in real-time systems, there is usually a requirement for their end-to-end latency. When mapped to software components, functional chains often result in communicating threads. Since threads are scheduled rather than tasks, specific task chain properties arise that can be exploited for response-time analysis. As a core contribution, this paper presents an extension of the busy-window analysis suitable for such task chains in static-priority preemptive systems. We evaluated the extended busy-window analysis in a compositional performance analysis using synthetic test cases and a realistic automotive use case showing far tighter response-time bounds than current approaches.

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Response-Time Analysis for Task Chains in Communicating Threads

Self-awareness has been used in many research fields in order to add autonomy to computing systems. In automotive systems, we face several system layers that must be enriched with self-awareness to build truly autonomous vehicles. This includes functional aspects like autonomous driving itself, its integration on the hardware/software platform, and among others dependability, real-time, and security aspects. However, self-awareness mechanisms of all layers must be considered in combination in order to build a coherent vehicle self-awareness that does not cause conflicting decisions or even catastrophic effects. In this paper, we summarize current approaches for establishing self-awareness on those layers and elaborate why self-awareness needs to be addressed as a cross-layer problem, which we illustrate by practical examples.

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Self-awareness in autonomous automotive systems

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.

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Platform-Centric Self-Awareness as a Key Enabler for Controlling Changes in CPS

For the development of complex software systems, we often resort to component-based approaches that separate the different concerns, enhance verifiability and reusability, and for which microkernel-based implementations are a good fit to enforce these concepts. Composing such a system of several interacting software components will, however, lead to complex precedence and blocking relations, which must be taken into account when performing latency analysis. When modelling these systems by classical task graphs, some of these effects are obfuscated and tend to render such an analysis either overly pessimistic or even optimistic. We therefore firstly present a novel task (meta-)model that is more expressive and accurate w.r.t. these (functional) precedence and mutual blocking relations. Secondly, we apply the busy-window approach and formulate a modular response-time analysis on task-chain level suitable but not restricted to static-priority scheduled systems. We show that the conjunction of both concepts allows the calculation of reasonably tight latency bounds for scenarios not adequately covered by related work.

https://dl.acm.org/doi/pdf/10.1145/3126505

Response-Time Analysis for Task Chains with Complex Precedence and Blocking Relations

Automotive control systems typically have latency requirements for certain cause-effect chains. When implementing and integrating these systems, these latency requirements must be guaranteed e.g. by applying a worst-case analysis that takes all indeterminism and limited predictability of the timing behaviour into account. In this paper, we address the latency analysis for multi-rate distributed cause-effect chains considering static-priority preemptive scheduling of offset-synchronised periodic tasks. We particularly focus on data age as one representative of the two most common latency semantics. Our main contribution is an Mixed Integer Linear Program-based optimisation to select design parameters (priorities, task-to-processor mapping, offsets) that minimise the data age. In our experimental evaluation, we apply our method to two real-world automotive use cases.

/pdf/data-age-analysis-and-optimisation-for-cause-effect-chains-2j01i6e585.pdf

Johannes Schlatow

Papers

Response-Time Analysis for Task Chains in Communicating Threads

Self-awareness in autonomous automotive systems

Platform-Centric Self-Awareness as a Key Enabler for Controlling Changes in CPS

Response-Time Analysis for Task Chains with Complex Precedence and Blocking Relations

Data-Age Analysis and Optimisation for Cause-Effect Chains in Automotive Control Systems