TY - GEN
T1 - Execution Trace Analysis for a Precise Understanding of Latency Violations
AU - Zoor, Maysam
AU - Apvrille, Ludovic
AU - Pacalet, Renaud
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/1/1
Y1 - 2021/1/1
N2 - Despite the amount of proposed works for the verification of diverse model properties, understanding the root cause of latency requirements violation in execution traces is still an open-issue especially for complex HW/SW system-level designs: is it due to an unfavorable real-time scheduling, to contentions on buses, to the characteristics of functional algorithms or hardware components? This identification is particularly at stake when adding new features in a model, e.g., a new security countermeasure. The paper introduces PLAN, a new trace analysis technique whose objective is to classify execution transactions according to their impact on latency. To do so, we rely first on a model transformation that builds up a dependency graph from an allocation model, thus including hardware and software aspects of a system model. Then, from this graph and an execution trace, our analysis can highlight how software or hardware elements contributed to the latency violation. The paper first formalizes the problem before applying our approach to simulation traces of SysML models. A case study defined in the AQUAS European project illustrates the interest ofour approach.
AB - Despite the amount of proposed works for the verification of diverse model properties, understanding the root cause of latency requirements violation in execution traces is still an open-issue especially for complex HW/SW system-level designs: is it due to an unfavorable real-time scheduling, to contentions on buses, to the characteristics of functional algorithms or hardware components? This identification is particularly at stake when adding new features in a model, e.g., a new security countermeasure. The paper introduces PLAN, a new trace analysis technique whose objective is to classify execution transactions according to their impact on latency. To do so, we rely first on a model transformation that builds up a dependency graph from an allocation model, thus including hardware and software aspects of a system model. Then, from this graph and an execution trace, our analysis can highlight how software or hardware elements contributed to the latency violation. The paper first formalizes the problem before applying our approach to simulation traces of SysML models. A case study defined in the AQUAS European project illustrates the interest ofour approach.
KW - Dependency Graph
KW - Embedded Systems
KW - Execution Trace Analysis
KW - MBSE
KW - Simulation
KW - Timing analysis
U2 - 10.1109/MODELS50736.2021.00021
DO - 10.1109/MODELS50736.2021.00021
M3 - Conference contribution
AN - SCOPUS:85123425555
T3 - Proceedings - 24th International Conference on Model-Driven Engineering Languages and Systems, MODELS 2021
SP - 123
EP - 133
BT - Proceedings - 24th International Conference on Model-Driven Engineering Languages and Systems, MODELS 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 24th ACM/IEEE International Conference on Model-Driven Engineering Languages and Systems, MODELS 2021
Y2 - 10 October 2021 through 15 October 2021
ER -