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Dagstuhl Seminar 07101

Quantitative Aspects of Embedded Systems

( Mar 04 – Mar 09, 2007 )

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1. Summary

1.1 Current status

Embedded software controls the core functionality of many systems. Embedded software is omnipresent: it controls telephone switches and satellites, drives the climate control in our offices and cars, runs pacemakers, is at the heart of our power plants, and makes our cars and TVs work properly. As such systems are massively encroaching on daily life, our reliance on embedded software is growing rapidly. But, how justifiable is this reliance?

Whereas traditional software has a rather transformational nature mapping input data onto output data, embedded software is different in many respects. Most importantly, embedded software is subject to complex and permanent interactions with their---mostly physical---environment via sensors and actuators. Typically software in embedded systems does not terminate and interaction usually takes place with multiple concurrent processes at the same time. Reactions to the stimuli provided by the environment should be prompt (timeliness or responsiveness), i.e., the software has to ``keep up'' with the speed of the processes with which it interacts.

Furthermore, characteristic for embedded systems is that they have to meet a multitude of quantitative constraints. These constraints involve the resources that a system may use (computation resources, power consumption, memory usage, communication bandwidth, costs, etc.), assumptions about the environment in which it operates (task arrival rates, task sizes), and requirements on the services that the system has to provide (timing constraints, performance, response time) and requirements of the continuity with which these services are delivered (availability, dependability, fault tolerancy, etc.).

The observed difference between traditional software and embedded software has recently led to initiatives for dedicated international conferences such as EMSOFT (since 2001), and ACM SIGPLAN Workshop on Languages, Compilers, and Tools for Embedded Systems (since 1998), as well as journals such as ACM Transactions on Embedded Computing Systems, (since 2002) and Design Automation for Embedded Systems Journal (since 2003). In various countries research institutes on embedded systems have been set up with a strong industrial cooperation, for instance, in Denmark (CISS), the Netherlands (ESI), and the US (CHESS).

1.2 A lack of quantitative assessment

Despite the importance of the quantitative constraints for the well-operation of embedded systems, the proper assessment of cost, resources, performance, dependability, robustness, etc., often comes as an afterthought. It is rather common for embedded software to be fully designed and functionally tested before any attempt is undertaken to determine its performance, dependability or resource-usage characteristics. One of the main reasons for this situation is that well-developed and rigorous evaluation techniques for non-functional, i.e., quantitaive system aspects have not become an integral part of standard software engineering practice. This undesirable situation has led to the increased interest by embedded software researchers to extend the usual functional specification and properties with a set of ``performance indices'', e.g., stated in terms of costs, timeliness, speed and the like, and constraints on these indices. Also in industry, a growing interest in assessing non-functional aspects of embedded systems as early as possible in the system design life cycle can be witnessed.

2. Where are we going?

Model-Driven Development (MDD) is a new software development technique in which the primary software artifact is a model. Ideally, the MDD technique allows engineers to (graphically) model the requirements, behaviour and functionality of computer-based systems. The design is iteratively analysed, validated, and tested throughout the development process, and automatically generated production-quality code can be output in a variety of languages.

Existing MDD tools for embedded systems are rather sophisticated in handling functional requirements but their treatment of quantitative constraints is still in development. Although methods for verification of real-time system designs, using for instance timed automata, are being developed, these methods are not yet mature enough for dealing with larger industrial embedded systems. Hence, MDD will not realise its full potential in the embedded systems area unless the ability to handle quantitative properties is drastically improved.

In contract to the situation in the design of embedded software systems, in the design of computer-communication systems, quantitative methods to determine the quality of the system, expressed in terms of throughput or response time have been used for a long time. Next to methods from classical queueing theory and discrete-event simulation, recently the use of analytical/numerical methods for evaluating complex systems has become more widespread. This has lead to methods and techniques to specify complex system behaviour using some formal method (Petri nets, process algebra) enhanced with time and probabilities. Subsequently, appropriate (Markovian) models are generated from these high-level models, which, after numerical analysis, provide detailed insight in performance and dependability measures of interest. More recently, also contraints related to power usage have been taken up.

Over the last, say, 5 years, very good progress has been made in pairing the above quantitative techniques to techniques known from the verification area, esp. model checking of properties specified in logics like CSL (an extension of CTL with stochastic time). This has lead to model checking algorithms and tools for Markovian models of system. The state-of-the-art, however, is still such that expert knowledge is required to use these techniques, hence, large-scale application in embedded software system design and implementation is still a dream rather than a reality.

Of course, also known quantitative techniques from the area of real-time systems (classical ones, such as EDF, or more advanced compositional ones), or methods known from network calculus (originally developed for dimensioning communication networks at a high level of abstraction), data flow graphs, and so on, can, and probably should be used as part of the embedded system design.

What is clear, though, is that all of the above techniques can only be used during the design of embedded systems after appropriate adaptation and embedding in a design trajectory, e.g., based on MMD.

Furthermore, were each of the above mentioned approaches has its strengths and weaknesses, an important first task is to map these strength and weaknesses (applicability, scope, modelling power, costs of evaluation, etc.). A second and more challenging question is then how to combine or integrate these methods. Such questions can only be answered when key researchers for these various approaches come together and exchange and discuss their ideas.

3. Seminar goal

Given the above considerations, the goal of this Dagstuhl seminar has been to bring together experts in the areas of embedded software design and implementation, model-based analysis of quantitative system aspects, and researchers working on extending all kinds of formal (design and analysis) methods with quantitative system aspects. These three areas are clearly well- related in the context of embedded systems, but have not been addressed as such in the past, as they have been worked upon in different communities. Thus, the seminar will lay bridges between these three areas, so that knowledge and experience can be shared, transferred and, ultimately, be generated.

  • Sebastian Altmeyer (Universität des Saarlandes, DE) [dblp]
  • Henrik Bohnenkamp (RWTH Aachen, DE)
  • Hichem Boudali (University of Twente, NL)
  • Lucia Cloth (University of Twente, NL)
  • Pepijn Crouzen (Universität des Saarlandes, DE)
  • Stephen A. Edwards (Columbia University - New York, US) [dblp]
  • Matthias Fruth (University of Birmingham, GB)
  • Reinhard German (Universität Erlangen-Nürnberg, DE) [dblp]
  • Tingting Han (RWTH Aachen, DE)
  • Boudewijn Haverkort (University of Twente, NL) [dblp]
  • Holger Hermanns (Universität des Saarlandes, DE) [dblp]
  • David N. Jansen (RWTH Aachen, DE) [dblp]
  • Marijn R. Jongerden (University of Twente, NL) [dblp]
  • Bengt Jonsson (Uppsala University, SE) [dblp]
  • Joost-Pieter Katoen (RWTH Aachen, DE) [dblp]
  • Mark Kattenbelt (University of Birmingham, GB)
  • Hermann Kopetz (TU Wien, AT)
  • Kim Guldstrand Larsen (Aalborg University, DK) [dblp]
  • Jaejin Lee (Combitech AB - Jönköping, SE)
  • Jan Madsen (Technical University of Denmark - Lyngby, DK) [dblp]
  • Peter Marwedel (TU Dortmund, DE) [dblp]
  • Pieter J. Mosterman (The MathWorks Inc. - Natick, MA USA, US) [dblp]
  • Martin Neuhaeusser (RWTH Aachen, DE)
  • Vladimir Okulevich (OOO Siemens CT - St. Petersburg, RU)
  • Jean-Francois Raskin (Free University of Brussels, BE) [dblp]
  • Michael Reichhardt Hansen (Technical University of Denmark - Lyngby, DK)
  • Jan Reineke (Universität des Saarlandes, DE) [dblp]
  • Anne Remke (University of Twente, NL) [dblp]
  • Wolfgang Rosenstiel (Universität Tübingen, DE)
  • Bastian Schlich (RWTH Aachen, DE) [dblp]
  • Gerard J. M. Smit (University of Twente, NL) [dblp]
  • Marielle Stoelinga (University of Twente, NL) [dblp]
  • Jürgen Teich (Universität Erlangen-Nürnberg, DE) [dblp]
  • Lothar Thiele (ETH Zürich, CH) [dblp]
  • Reinhard Wilhelm (Universität des Saarlandes, DE) [dblp]
  • Daniel Willems (RWTH Aachen, DE)
  • Jin Hyuk Yoon (Seoul National University, KR)
  • Lijun Zhang (Universität des Saarlandes, DE) [dblp]

  • modelling / simulation
  • data structures / algorithms / complexity
  • optimization / scheduling
  • programming languages / compiler
  • sw-engineering
  • semantics / formal methods
  • verification / logic
  • embedded systems

  • performance/reliability
  • embedded systems
  • software
  • model-driven design