Herman Kelder has joined the DSE2.0 research project as a master student. DSE2.0 is a project that aims to propose a methodology for design-space exploration of complex distributed cyber-physical systems, like lithography machines manufactured by ASML. One of the great challenges is to improve the scalability to handle the complexity of such systems, a challenge that needs to be addressed both in terms of how the system (performance) is modelled and evaluated (simulated) for a particular design point, as well as how design points to evaluate is being chosen. Hermans thesis will focus on how to improve the scalability of system-level simulation to allow more design points to be evaluated faster.
One of Herman’s first assignments was to put together a literature review on this topic. The literature review, entitled “Exploring Scalability in System-Level Simulation Environments for Distributed Cyber-Physical Systems“, investigates state-of-the-art scalability techniques for system-level simulation environments, i.e. Simulation Campaigns, Parallel Discrete Event Simulations (PDES), and Hardware Accelerators. The goal is to address the challenge of scalable Design Space Exploration (DSE) for dCPS, discussing such approaches’ characteristics, applications, advantages, and limitations. The conclusion recommends starting with simulation campaigns as those provide increased throughput, adapt to the number of tasks and resources, and are already implemented by many state-of-the-art simulators. Nevertheless, further research has to be conducted to define, implement, and test a sophisticated general workflow addressing the diverse sub-challenges of scaling system-level simulation environments for the exploration of industrial-size distributed Cyber-Physical Systems.
We look forward to working with Herman and seeing how his research develops along these directions.
I am pleased to announce that our position paper “Design Space Exploration for Distributed Cyber-Physical Systems: State-of-the-art, Challenges, and Directions” has been accepted for publication at the Euromicro Conference on Digital System Design (DSD). This is the first accepted paper from the DSE2.0 project, a collaboration between University of Amsterdam, Leiden University, and ASML. The project is a part of the Mastering Complexity (MasCot) partnership program funded by ESI.
The paper addresses the challenge of designing industrial cyber-physical systems (CPS), which are often complex, heterogeneous, and distributed computing systems that typically
integrate and interconnect a large number of hardware and software components. Producers of these distributed Cyber-Physical Systems (dCPS) require support for making (early) design decisions to avoid expensive and time consuming oversights. This calls for efficient and scalable system-level Design Space Exploration (DSE) methods for dCPS. In this position paper, we review the current state of the art in DSE, and argue that efficient and scalable DSE technology for dCPS is more or less non-existing and constitutes a largely unchartered research area. Moreover, we identify several research challenges that need to be addressed and discuss possible directions for targeting such DSE technology for dCPS.
I am happy to announce that the paper “Partial Specifications of Component-based Systems using Petri Nets” has been accepted for publication at the International Workshop on Petri Nets and Software Engineering (PNSE) 2022. This paper was first-authored by Bart-Jan Hilbrands, a (former) student in the Master of Software Engineering program at the University of Amsterdam, who did his master thesis project under the supervision of myself and my ESI colleague Debjyoti Bera. The master thesis project was conducted in the context of the DYNAMICS project, a bi-lateral research project between ESI and Thales, which looked into specification, verification, and adaptation of software interfaces. This publication is a good example of how a good master thesis can be turned into a publication.
The paper addresses the problem of verifying correctness properties, such as absence of deadlocks, livelocks, and buffer overflows, in software components with multiple inter-dependent interfaces. An approach based on partial specification of dependencies between interfaces, expressed as a set of functional constraints, is proposed in the paper. The papers presents and formalizes three commonly occurring functional constraints and provides algorithms for encoding them into a Petri net representation of the interfaces, enabling interface verification through reachability analysis. The approach has been implemented and demonstrated using ComMA.
It has been almost a year since we published the first survey-based study in the area of real-time systems at the Real-time Systems Symposium (RTSS) back in December 2020. The paper was entitled “An Empirical Survey-based Study into Industry Practice in Real-time Systems” and had the ambitious goal of addressing the gap between academic research and industry practice through an empirical survey-based study into industry practice. The survey had five objectives: 1) Establish whether timing predictability is of concern to the real-time embedded systems industry, 2) Identify relevant industrial problem contexts, including hardware platforms, middleware, and software, 3) Determine which methods and tools are used to achieve timing predictability, 4) Establish which techniques and tools are used to satisfy real-time requirements, and 5) Determine trends for future real-time systems. The survey was completed by 120 industry practitioners in the field of real-time embedded systems, which allowed interesting observations and insights to be made about the characteristics of the systems being developed today and important trends for the future.
Now, almost one year later, I am happy to announce that an extended version of our RTSS paper has been accepted for publication in the Real-time Systems journal. The title of the article is “A Comprehensive Survey of Industry Practice in Real-Time Systems“. The main extensions in the article include: 1) a discussion of potential threats to validity of the survey and its results, as well as the steps taken to mitigate them, 2) a statistical analysis and discussion of the results of the survey, in the context of its five objectives, 3) a discussion of the results of a quiz aimed at determining if the aggregate findings of the survey are common knowledge in the real-time systems community. In addition, more aggregated data from the survey has been released, allowing interested readers to further delve into the similarities and differences between the avionics, automotive, and consumer electronic domains.
The paper describes ESI’s current view on the field of System-Performance Engineering (SysPE). SysPE encompasses modeling formalisms, methods, techniques, and industrial practices to design systems for performance, where performance is taken integrally into account during the whole system life cycle. Industrial SysPE state of practice is generally model-based. Due to the rapidly increasing complexity of systems, there is a need to develop and establish model-driven methods and techniques. To structure the field of SysPE, the paper identifies: (1) industrial challenges motivating the importance of SysPE, (2) scientific challenges that need to be addressed to establish model-driven SysPE, (3) important focus areas for SysPE and (4) best practices. A survey was conducted to collect feedback on our views. The responses were used to update and validate the identified challenges, focus areas, and best practices. The final result is presented in this paper. Interesting observations are that industry sees a need for better design-space exploration support, more than for additional performance modeling and analysis techniques. Also tools and integral methods for SysPE need attention. From the identified focus areas, scheduling and supervisory control is seen as lacking established best practices.
The paper will be presented as a part of Industry Session 2 at ESWEEK on October 12. The second talk of that session presents why and how ITEC, Nexperia, a world-leading manufacturer of semiconductor equipment, is moving towards model-driven system-level development. The session ends with a moderated Q&A. Since ESWEEK is an online event this year, you can register for 20 USD if you want to attend the conference and the session.
Update: The video of the Industry session is now available:
The accepted paper is entitled “Synthetic Portnet Generation with Controllable Complexity for Testing and Benchmarking” and presents a heuristic-driven method for synthetic generation of random portnets, a kind of Petri Nets suitable for modelling software interfaces in component-based systems. The method considers three user-specified complexity parameters: the expected number input and output places, and the prevalence of non-determinism in the skeleton of the generated net. An implementation of this method is available as an open-source Python tool. Experiments demonstrate the relations between the three complexity parameters and investigate the boundaries of the proposed method. This work was helpful for the DYNAMICS project, as it allowed us to synthetically generate a large number of interfaces of varying complexity that we could use to evaluate the scalability of existing academic tools for adapter generation.
It is my great pleasure to announce that our paper “An Empirical Survey-based Study into Industry Practice in Real-time Systems” has appeared at the 41st IEEE Real-Time Systems Symposium (RTSS). The paper presents results and observations from a survey of 120 industry practitioners in the field of real-time embedded systems. The survey provides insights into the characteristics of the systems being developed today and identifies important trends for the future. The survey aims to inform both academics and practitioners, helping to avoid divergence between industry practice and fundamental academic research.
This work is a dear pet project of mine that has been a long time in the making. Once I joined ESI (TNO), I started reading papers and attending conferences in the modelling community. I came across empirical survey-based research that systematically investigated industry trends, needs and practices, and that studied adoption and perceived benefits and drawbacks of different technologies and methodologies. I immediately found this line of work incredibly useful as it elevated my understanding of what happened in industry from a collection of anecdotes based on conversations with a few people in a few companies to something that could capture the experience of hundreds of people across industrial domains. I also had the feeling that this line of work provided all the citations I needed for the introduction of my papers, as it helped me position my own work on modelling in a broader industrial reality.
Empirical research is an established research direction in social science, but also in technical fields, such as software engineering and to a lesser extent system engineering. However, there was no work like this in the area of real-time systems. I decided to change this and pitched the idea to Rob Davis, Mitra Nasri, and Geoffrey Nelissen and Sebastian Altmeyer during a meeting in Amsterdam in May 2019. They substantially improved on my ideas and did a lot of very good work and almost a year and a half later, the paper is available for you to read. We could not fit everything we had to say into the RTSS paper, so there is also a supporting technical note entitled “A Comprehensive Survey of Industry Practice in Real-time Systems“.
RTSS Session
A separate session was dedicated to this work on the last day of RTSS 2020. The session began with a 25 minute paper presentation, which is available here:
The paper presentation was followed by a panel discussion involving three industry practitioners from the three main industrial domains covered by the survey: Marcelo Lopez Ruiz (Microsoft), representing the consumer electronics industry, Simon Schliecker (Volkswagen), representing automotive, and Stephen Law (Rolls-Royce), providing an avionics perspective. The panel discussed four key questions relating to the survey results:
Q1. What important characteristics of real-time systems highlighted in the survey results are the most relevant with respect to your industry? And what other important characteristics are there that were not picked up by the survey?
Q2. What are the most relevant trends in real-time systems development in your industry now, and looking ahead over the next 10 years?
Q3. Did anything surprise you in the survey and its results? And why?
Q4. Given the results of the survey, and your own experience, what recommendations would you to give to the academic community? Which areas should we work more or less on? What assumptions should we make or not make?
The opening statements from the panelists related to the four questions was pre-recorded and followed by a live discussion. The pre-recorded part of the panel is available here:
The session finished after one hour, before there was time to take questions from the audience. A separate Zoom room was created for this purpose and to allow the interaction to continue, which it did for another hour! We were very pleased with the interest in this paper and in the session.
Emerging Research Direction
I hope that this work is the first of many empirical research papers in real-time systems. There are many ways to continue with this line of work. First of all, others need to replicate our results to validate that they hold for different populations. For this purpose, we will be happy to transfer the survey we made on SurveyMonkey, such that it can be reused. Secondly, our survey was very broad and covers real-time systems across many application domains. More specific questions could be obtained if the focus was on a single domain, although the main challenge will be finding enough representative participants with a narrow focus. Thirdly, surveys are only one way of conducting empirical research. Another method sometimes used in software engineering is to use interviews, allowing more in-depth questions to be asked. However, the drawback of this method is that it is more time consuming to interview are large number of participants and to encode and analyze the results.
This direction in real-time system research is just emerging and we hope it will grow and become a well-established part of the research conducted in the community. This would help us better understand the industry we are trying to serve and help us close the gap between academic research and industry practice. A first important step is that this direction is recognized by all main conferences and journals in the area of real-time systems and explicitly included in the call for papers. You can play an important part here by helping us communicate the value of empirical research to others in our community and beyond.
An article entitled “Response Time Analysis of Multiframe Mixed-Criticality Systems with Arbitrary Deadlines” has been accepted for publication in Real-time Systems journal. This work is first authored by Ishfaq Hussain and is another collaboration with my former colleagues at CISTER. The article extends our RTNS 2019 paper “Response Time Analysis of Multiframe Mixed-Criticality Systems” that received both an Outstanding Paper Award and a Best Student Paper Award. The RTNS paper presented a schedulability analysis for the multi-frame mixed-criticality model, extending the static and dynamic analysis techniques for mixed-criticality scheduling and the schedulability analysis for multi-frame task systems.
The accepted journal article extends the RTNS paper by generalizing the proposed schedulability analyses from a constrained-deadline task model to the more general, but also more complex, model with arbitrary deadlines. The corresponding optimal priority assignment for our schedulability analysis is also identified. In experiments with synthetic workloads, the proposed analyses are compared in terms of scheduling success ratio, against the frame-agnostic analyses for the corresponding variants of the Vestal model.
The paper addresses the problem of satisfying real-time requirements in industrial systems using unpredictable hardware and software, which limit or entirely prevent the application of established real-time analysis techniques. To this end, we propose PReGO, a generative methodology for satisfying real-time requirements in industrial commercial-off-the-shelf (COTS) systems. We report on our experience in applying PReGO to a use-case: a Search & Rescue application running on a fixed-wing drone with COTS components, including an NVIDIA Jetson board and a stock Ubuntu/Linux. We empirically evaluate the impact of each integration step and demonstrate the effectiveness of our methodology in meeting real-time application requirements in terms of deadline misses and energy consumption.
Anna Minaeva, who recently received her PhD degree, just had a journal article entitled “Control Performance Optimization for Application Integration on Automotive Architectures” accepted for publication in IEEE Transactions on Computers. This article is the result of a HiPEAC collaboration grant that Anna was awarded back in 2016 to visit the group of Samarjit Chakraborty at TU Munich. I am very happy to see that this grant resulted in a joint publication in a prestigious journal and hope to collaborate with Samarjit again in the future.
The article addresses the problem of generating a time-triggered schedule for a number of independently developed automotive applications on a number of shared resources, such that their control performance only suffers minimal degradation. The three main contributions are: 1) a constraint programming model that solves the problem optimally, exploiting properties of the problem to reduce the computation time; 2) a fast heuristic called Flexi that only has a minor impact on the optimality of the solution; and 3) an experimental evaluation of the scalability and efficiency of the proposed approaches on a case study, in addition to several synthetic datasets. The results show that the heuristic provides a solution on average 5 times faster, finding a feasible solution in 31% more problem instances than the optimal approach within a time limit, while only sacrificing 0.5% of the control performance quality for the largest dataset.
