Perturbation tracking

Williams, K. J. Orton; Andersland, Mark S.; Gannon, J.A.; Lumpp, James E.; Casavant, Thomas L.

doi:10.1109/cdc.1993.325063

Cited by 9 publications

(5 citation statements)

References 8 publications

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“…Although our definition of invertibility above applies to timed automata, and is different from that of 0zveren and Willsky (1992), the two notions are in fact related, as we shall see in Lemma 2.5. Similar notions of invertibility for timed Petri Nets were considered in Chong (1993d, 1993e) and Williams et al (1993).…”

Section: Definitionmentioning

confidence: 89%

Distributed inversion in timed discrete event systems

Park

Chong

1995

Discrete Event Dyn Syst

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Abstract. We consider a discrete event system (DES) modeled by a timed automaton with partial state and event observations. We view the system as an input-output system, where the input is a sequence of event lifetimes, and the output is the resulting sequence of events, states, and transition epochs. We consider the problem of extracting event lifetimes (input) from observations of the output trajectory, which we call inversion. We give necessary and sufficient conditions for invertibility, and an algorithm that extracts event lifetimes from any given output observation of an invertible system. We describe a distributed timed DES model based on the prioritized synchronous product of subsystems, and study the inversion problem in this framework. We show that invertibility in the subsystems implies invertibility in the global system. To illustrate our results, we provide an example of a tandem network.

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Section: Definitionmentioning

confidence: 89%

Distributed inversion in timed discrete event systems

Park

Chong

1995

Discrete Event Dyn Syst

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“…Several researchers have developed techniques to attempt to remove overheads from the reported data [14,20,69,72,76]. Yan and Listgarten [76] specifically addressed the overhead of writing the trace buffer to disk in AIMS by generating an event marker for these write operations and removing the overhead in a postprocessing step.…”

Section: Perturbationmentioning

confidence: 99%

Scalable event tracking on high-end parallel systems

Mohror¹

2000

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Accurate performance analysis of high end systems requires event-based traces to correctly identify the root cause of a number of the complex performance problems that arise on these highly parallel systems. These high-end architectures contain tens to hundreds of thousands of processors, pushing application scalability challenges to new heights. Unfortunately, the collection of event-based data presents scalability challenges itself: the large volume of collected data increases tool overhead, and results in data files that are difficult to store and analyze. Our solution to these problems is a new measurement technique called trace profiling that collects the information needed to diagnose performance problems that traditionally require traces, but at a greatly reduced data volume. The trace profiling technique reduces the amoun! of data measured and stored by capitalizing on the repeated behavior of programs, and on the similarity of the behavior and performance of parallel processes in an application run. Trace profiling is a hybrid between profiling and tracing, collecting summary information about the event patterns in an application run. Because the data has already been classified into behavior categories, we can present reduced, partially analyzed performance data to the user, highlighting the performance behaviors that comprised most of the execution time.

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“…Observation data is used in a wide variety of contexts, including supervisory control [17], [9], [4], fault testing and diagnosis [18], [8], [1], performance analysis and enhancement [3], and correctness checking [20]. In some situations, only logical information is needed (e.g., supervisory control), while other situations require both logical and timing information (e.g., performance analysis).…”

Section: Introductionmentioning

confidence: 99%

On d-inversion in interruptive timed discrete-event systems

Park

Chong²

1997

IEEE Trans. Automat. Contr.

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The authors consider the problem of extracting event lifetimes from partial observations of an interruptive timed discrete-event system. The extraction of the lifetime of an occurring event is based on observations of all previous transitions and d subsequent transitions. We refer to this notion as d-inversion. We give necessary and sufficient structural conditions for an event to be d-invertible in a given system.

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Perturbation tracking

Cited by 9 publications

References 8 publications

Distributed inversion in timed discrete event systems

Distributed inversion in timed discrete event systems

Scalable event tracking on high-end parallel systems

On d-inversion in interruptive timed discrete-event systems

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