Networks of real-time processes

Yates, Robert K.

doi:10.1007/3-540-57208-2_27

Cited by 23 publications

(33 citation statements)

References 6 publications

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“…We use it here to group together a number of formalisms which are based on concurrent actors manipulating streams of timed events, such as DEVS [34], real-time process networks [33], or actor theories [14]. 3 The DE paradigm is prevalent in simulation frameworks in di↵erent application domains, from queueing systems and networks to circuits.…”

Section: Discrete-event Formalismsmentioning

confidence: 99%

Viewpoints, formalisms, languages, and tools for cyber-physical systems

Broman

Lee

Tripakis

et al. 2012

Proceedings of the 6th International Workshop on Multi-Paradigm Modeling

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Cyber-physical systems (CPS) are becoming indispensable in our modern way of life. As an application domain CPS is not new. As an intellectual discipline, however, it is. This paper focuses on CPS modeling, which is an essential activity in CPS design, with multiple challenges. In particular, stakeholders lack a systematic framework and guidelines to help them choose among the many available modeling languages and tools. We propose such a framework in this paper. Our framework consists of three elements: viewpoints, which capture the stakeholders' interests and concerns; concrete languages and tools, among which the stakeholders must make a selection when defining their CPS design environments; and abstract, mathematical formalisms, which are the "semantic glue" linking the two worlds. As part of the framework, we survey various formalisms, languages, and tools and explain how they are related. We also provide examples of viewpoints and discuss how they are related to formalisms.

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Section: Discrete-event Formalismsmentioning

confidence: 99%

Viewpoints, formalisms, languages, and tools for cyber-physical systems

Broman

Lee

Tripakis

et al. 2012

Proceedings of the 6th International Workshop on Multi-Paradigm Modeling

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“…A timed process network model is a model which not only describes the data transformations of the network, but also the timing of such a process [11], [52]. Such a model is typically made by labeling symbols with time-stamps or tags from a particular time domain of choice (for instance non-negative integers or reals) or, more general, according to the tagged-signal model of [32], allowing for instance also partially ordered or super-dense time domains common in hardware description languages [34].…”

Section: Timementioning

confidence: 99%

“…Using time information, decisions can be taken in a deterministic way, based on the time-stamps of data, for instance to describe a merge process which merges symbols in the order in which they arrive (with some provision, for instance fixed priority, when tokens arrive at the same time) [11], [52], [34]. Alternative network equations can be formulated and different fixed-point theorems (such as Kleene's or Banach's) can be used to show them to have a unique solution.…”

Section: Timementioning

confidence: 99%

Kahn Process Networks and a Reactive Extension

Geilen

Basten

2010

Handbook of Signal Processing Systems

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Kahn and MacQueen have introduced a generic class of determinate asynchronous data-flow applications, called Kahn Process Networks (KPNs) with an elegant mathematical model and semantics in terms of Scott-continuous functions on data streams together with an implementation model of independent asynchronous sequential programs communicating through FIFO buffers with blocking read and non-blocking write operations. The two are related by the Kahn Principle which states that a realization according to the implementation model behaves as predicted by the mathematical function. Additional steps are required to arrive at an actual implementation of a KPN to take care of scheduling of independent processes on a single processor and to manage communication buffers. Because of the expressiveness of the KPN model, buffer sizes and schedules cannot be determined at design time in general and require dynamic run-time system support. Constraints are discussed that need to be placed on such system support so as to maintain the Kahn Principle. We then discuss a possible extension of the KPN model to include the possibility for sporadic, reactive behavior which is not possible in the standard model. The extended model is called Reactive Process Networks. We introduce its semantics, look at analyzability and at more constrained data-flow models combined with reactive behavior.

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“…In stream-oriented dataflow models, our causality interface can be used to analyze compositions of actors for deadlock [Broy and Stefanescu 2001;Lee and Parks 1995]. In discrete-event models, it can be used to ensure deterministic processing of simultaneous events, and to identify causality loops [Lee 1999;Yates 1993]. In synchronous languages, it can be used to identify whether a combinational cycle has a reactive and deterministic behavior for all possible combinations of input values [Schneider et al 2004;Berry 1996;Edwards and Lee 2003].…”

Section: Introductionmentioning

confidence: 99%

Causality interfaces for actor networks

Zhou

Lee

2008

ACM Trans. Embed. Comput. Syst.

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We consider concurrent models of computation where "actors" (components that are in charge of their own actions) communicate by exchanging messages. The interfaces of actors principally consist of "ports," which mediate the exchange of messages. Actor-oriented architectures contrast with and complement object-oriented models by emphasizing the exchange of data between concurrent components rather than transformation of state. Examples of such models of computation include the classical actor model, synchronous languages, dataflow models, process networks, and discrete-event models. Many experimental and production languages used to design embedded systems are actor oriented and based on one of these models of computation. Many of these models of computation benefit considerably from having access to causality information about the components. This paper augments the interfaces of such components to include such causality information. It shows how this causality information can be algebraically composed so that compositions of components acquire causality interfaces that are inferred from their components and the interconnections. We illustrate the use of these causality interfaces to statically analyze timed models and synchronous language compositions for causality loops and dataflow models for deadlock. We also show that that causality analysis only needs to be performed for one port in each directed communication cycle, and we give a conservative approximation technique for handling dynamically changing causality properties.

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Networks of real-time processes

Cited by 23 publications

References 6 publications

Viewpoints, formalisms, languages, and tools for cyber-physical systems

Viewpoints, formalisms, languages, and tools for cyber-physical systems

Kahn Process Networks and a Reactive Extension

Causality interfaces for actor networks

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