EvoSMS: An Event-Oriented Simulation Method for Multi-Core Real-Time Scheduling

In this paper, an event-oriented simulation analysis approach was established for the schedulability analysis of multi-core embedded systems. To the best of our knowledge, this work is the first to introduce an event-oriented simulation strategy into multi-core processor schedulability analysis. The proposed method transforms the conventional step-by-step simulation into a jump-based simulation process, thereby reducing redundant system state updates and improving computational efficiency. Task execution is abstracted as a time-axis folding operation, and two algorithms, RBTF and PBTF, are developed to optimize event generation and management, effectively lowering the algorithmic complexity of the simulation. Furthermore, to resolve core-mapping conflicts arising in global scheduling during jump-based simulation, a time-axis-unfolding-based dynamic core-mapping algorithm is proposed. By incorporating a time interval recycling mechanism and a priority inheritance algorithm, this method ensures correct job-to-core assignment and enables precise response time analysis under global scheduling policies. This approach reduced the simulation time by 96.24% on average while guaranteeing the accuracy and soundness of the analysis. It is primarily applicable to periodic tasks with implied deadlines, and future work will explore extending this method to support sporadic tasks and tasks with arbitrary deadlines.

Authors

Institutions

Publication Details

Journal
Applied Sciences
Published
2025-10-22
DOI
https://doi.org/10.3390/app152111313
Primary Topic
Real-Time Systems Scheduling
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

EvoSMS: An Event-Oriented Simulation Method for Multi-Core Real-Time Scheduling

J.C. Zhang, Fei Fan, Xianchen Shi, Yongping Pu
Applied Sciences
Real-Time Systems Scheduling
article

EvoSMS: An Event-Oriented Simulation Method for Multi-Core Real-Time Scheduling

J.C. Zhang, Fei Fan, Xianchen Shi, Yongping Pu
article en

Abstract

In this paper, an event-oriented simulation analysis approach was established for the schedulability analysis of multi-core embedded systems. To the best of our knowledge, this work is the first to introduce an event-oriented simulation strategy into multi-core processor schedulability analysis. The proposed method transforms the conventional step-by-step simulation into a jump-based simulation process, thereby reducing redundant system state updates and improving computational efficiency. Task execution is abstracted as a time-axis folding operation, and two algorithms, RBTF and PBTF, are developed to optimize event generation and management, effectively lowering the algorithmic complexity of the simulation. Furthermore, to resolve core-mapping conflicts arising in global scheduling during jump-based simulation, a time-axis-unfolding-based dynamic core-mapping algorithm is proposed. By incorporating a time interval recycling mechanism and a priority inheritance algorithm, this method ensures correct job-to-core assignment and enables precise response time analysis under global scheduling policies. This approach reduced the simulation time by 96.24% on average while guaranteeing the accuracy and soundness of the analysis. It is primarily applicable to periodic tasks with implied deadlines, and future work will explore extending this method to support sporadic tasks and tasks with arbitrary deadlines.

Applied SciencesVol. 15(21)
Applications Research (United States) (US)
Openalex Percentile: Top 67%
Real-Time Systems Scheduling
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.