ProMARTES: accurate network and computation delay prediction for component-based distributed systems

K. Triantafyllidis, W. Aslam, Y. Bondarau, J.J. Lukkien, P.H.N. de With

Research output: Contribution to journalArticleAcademicpeer-review

2 Citations (Scopus)
4 Downloads (Pure)


This paper proposes a cycle-accurate performance analysis method for real-time component-based distributed systems (CB-RTDS).
The method involves the following phases:
(a) profiling SW components at cycle execution level and modeling the obtained performance measurements in MARTE-compatible component resource models,
(b) guided composition of the system architecture from available SW and HW components,
(c) automated generation of a system model, specifying both computation and network loads, and
(d) performance analysis (scheduling, simulation and network analysis) of the composed system model.
The method is demonstrated for a real-world case study of 3 autonomously navigating robots with advanced sensing capabilities.
The case study is challenging because of the SW/HW mapping, real-time requirements and data synchronization among multiple nodes.
This case-study proved that, thanks to the adopted low-level performance metrics, we are able to obtain accurate performance predictions of both computation and network delays.
Moreover, the combination of analytical and simulation analysis methods enables the computation of both the guaranteed Worst Case Execution Time (WCET) and the detailed execution time-line data for real-time tasks.
As a result, the analysis yields the identification of an optimal architecture, with respect to real-time deadlines, robustness and system costs.
The paper main contributions are the cycle-accurate performance analysis workflow and supportive open-source ProMARTES tool-chain, both incorporating a network prediction model in all the performance analysis phases.
Original languageEnglish
Pages (from-to)450-470
JournalJournal of Systems and Software
Publication statusPublished - 23 Apr 2016


  • simulation, scheduling, analysis, performance, real-time, distributed


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