Static Dataflow Analysis for Soft Real-Time System Design

  • Alexander Kocian
  • Stefano Chessa
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 801)


Synchronous (deterministic) dataflow (SDF) has been extensively used to model flow constraints of digital signal processing (DSP) applications executed on (hard) real-time (RT) operating system (OS). Modern internet-of-things are, however, are often equipped with (soft) RTOSs such as embedded Linux. To reduce design iterations for the latter, the paper proposes a stochastic approach to SDF graphs where the response time of each node is modeled as probability density function (pdf). With increasing number of iterations over the graph, the individual PDFs propagate through the network. The first and second central moments of the resulting joint pdf correspond to the expected system latency and jitter, respectively. The scheduler may execute the code sequentially or in parallel. The proposed analysis tool is helpful in identifying bottlenecks within the system.


Probability Soft real-time Service time Latency Jitter Synchronous density flow 


  1. 1.
    Bekooij, M., Hoes, R., Moreira, O., Poplavko, P., Pastrnak, M., Mesman, B., Mol, J.D., Stuijk, S., Gheorghita, V., van Meerbergen, J.: Dataflow Analysis for Real-Time Embedded Multiprocessor System Design, Philips Research, vol. 3. Springer, Dordrecht (2005)Google Scholar
  2. 2.
    Durfee, E., Rosenschein, J.: Distributed problem solving and multiagent systems: comparisons and examples. In: Proceedings of 13th International Workshop on Distributed Artificial Intelligence, pp. 94–104 (1994)Google Scholar
  3. 3.
    Hahm, O., Baccelli, E., Petersen, H., Tsiftes, N.: Operating systems for low-end devices in the internet of things: a survey. IEEE Internet Things J. 3(5), 720–734 (2016)CrossRefGoogle Scholar
  4. 4.
    Lee, E., Messerschmitt, D.G.: Synchronous data flow. Proc. IEEE 75(9), 1235–1245 (1987)CrossRefGoogle Scholar
  5. 5.
    Nadarajah, S., Kotz, S.: Exact distribution of the max/min of two Gaussian random variables. IEEE Trans. VLSI Syst. 16(2), 210–212 (2007)CrossRefGoogle Scholar
  6. 6.
    Papoulis, A.: Probability, Random Variables, and Stochastic Processes. McGraw Hill Inc. (1991)Google Scholar
  7. 7.
    Schaumont, P.R.: Data Flow Modeling and Transformation. Springer, Boston (2013)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of PisaPisaItaly

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