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Modeling and Analysis of Task Scheduling Strategies for Jitter Minimization in Real-Time Systems

Monisha M Reddy, Likitha G S, Madhumathy P.

Abstract


In real-time computing systems, maintaining a strict temporal determinism is as important as ensuring functional correctness. A primary pain-point in these architectures is timing jitter. Timing Jitter is defined as the difference in task execution intervals relative to a predefined isochronous schedule. Such latency variations can degrade the closed-loop control stability and network throughput in time critical applications.

This study is done using MATLAB simulation to evaluate scheduling heuristics with focus on temporal predictability. We analyze both time-triggered deterministic models and proba- bilistic non-deterministic models. While deterministic scheduling enforces a rigid periodic duty cycle, the non-deterministic model introduces stochastic noise to emulate real-world interrupt laten- cies and resource contention.

We quantify jitter through statistical signal analysis, measuring mean temporal drift, variance and worst-case timing error. Our comparative data reveals that deterministic scheduling preserves the phase alignment with minimal jitter. Whereas non- deterministic execution exhibits significant timing skew. This highlights why rigorous scheduling is essential for the operational integrity of time-sensitive systems.


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References


M. Di Natale and J. A. Stankovic, “Scheduling distributed real-time tasks with minimum jitter,” IEEE Transactions on Computers, vol. 49, no. 4, pp. 303–316, 2000.

M. Lluesma, A. Cervinand P. Balbastre, “Jitter evaluation of real-time control systems,” in Proc. IEEE RTCSA, 2006.

R. J. Bril, J. J. Lukkienand R. H. Mak, “Best-case response times and jitter analysis of real-time tasks,” in Proc. Real-Time Systems Conf., 2009.

C.-C. Lin et al., “Eliminating timing anomalies in scheduling periodic tasks with release jitter,” arXiv preprint, 2024.

K. Lee et al., “Jitter-based mixed-criticality scheduling for distributed real-time systems,” IEEE IoT Journal, vol. 6, no. 4, pp. 6310–6324, 2019.

Banerjee, I., Madhumathy, P. (2022). IoT Based Agricultural Business Model for Estimating Crop Health Management to Reduce Farmer Distress Using SVM and Machine Learning. In: Pattnaik, P.K., Kumar, R., Pal, S. (eds) Internet of Things and Analytics for Agriculture, Volume 3. Studies in Big Data, vol 99. Springer, Singapore. https://doi.org/10.1007/978-981-16-6210-2_8.

Prasad, S.B., Madhumathy, P. (2021). Long Term Evolution for Secured Smart Railway Communications Using Internet of Things. In: Das, S., Das, S., Dey, N., Hassanien, AE. (eds) Machine Learning Algorithms for Industrial Applications. Studies in Computational Intelligence, vol 907. Springer, Cham. https://doi.org/10.1007/978-3-030-50641-4_16.

Kavitha, N., Madhumathy, P., Prasad, R.M. et al. Machine learning technique for breast cancer detection and classification. Mach. Learn. Comput. Sci. Eng 1, 16 (2025). https://doi.org/10.1007/s44379-025-00018-y.

Suma M R, Madhumathy P, "An optimal swift key generation and distribution for QKD" scientific and technical journal of Information technologies, Machines and optics (Elsevier), Vol. 22 No 1, January –February 2022. DOI: 10.17586/2226-1494-2022-22-1-101-11.

P.Madhumathy, D.Sivakumar, (2012) “A comparative analysis of clustering based routing techniques for wireless sensor networks”, International Journal of Scientific and Engineering Research Volume 3, Number 10.


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