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Documents authored by Zhao, Jinhao

Document
DOI: 10.4230/LIPIcs.ECRTS.2026.21
Semi-Clairvoyant Scheduling for Jobs with Multiple Criticalities

Authors: Kunal Agrawal, Tung Duc Thai, and Jinhao Zhao

Published in: LIPIcs, Volume 375, 38th European Conference on Real-Time Systems (ECRTS 2026)

Abstract
This paper considers scheduling jobs semi-clairvoyantly in mixed-criticality systems. In the semi-clairvoyant model, unlike the non-clairvoyant model, we know the WCET mode of the job at job arrival. Prior work has only considered this model for dual criticality jobs. We consider this problem for an arbitrary number of criticalities. We prove that there exist semi-clairvoyant schedulers that guarantee schedulability with speedup (2^m-1)/2^{m-1} for systems with m criticality levels. In addition, we prove that this bound is tight by providing a job set construction that requires this speed for any semi-clairvoyant scheduler. Finally we provide a linear programming formulation that optimally schedules the semi-clairvoyant system of jobs. The number of variables and constraints is polynomial in the number of jobs, but exponential in the number of criticalities.
Cite as

Kunal Agrawal, Tung Duc Thai, and Jinhao Zhao. Semi-Clairvoyant Scheduling for Jobs with Multiple Criticalities. In 38th European Conference on Real-Time Systems (ECRTS 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 375, pp. 21:1-21:23, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{agrawal_et_al:LIPIcs.ECRTS.2026.21,
  author =	{Agrawal, Kunal and Thai, Tung Duc and Zhao, Jinhao},
  title =	{{Semi-Clairvoyant Scheduling for Jobs with Multiple Criticalities}},
  booktitle =	{38th European Conference on Real-Time Systems (ECRTS 2026)},
  pages =	{21:1--21:23},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-429-1},
  ISSN =	{1868-8969},
  year =	{2026},
  volume =	{375},
  editor =	{Kritikakou, Angeliki},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ECRTS.2026.21},
  URN =		{urn:nbn:de:0030-drops-266130},
  doi =		{10.4230/LIPIcs.ECRTS.2026.21},
  annote =	{Keywords: mixed-criticality, semi-clairvoyance, schedulers, linear programming}
}
Document
DOI: 10.4230/LIPIcs.ECRTS.2025.13
Faster Classification of Time-Series Input Streams

Authors: Kunal Agrawal, Sanjoy Baruah, Zhishan Guo, Jing Li, Federico Reghenzani, Kecheng Yang, and Jinhao Zhao

Published in: LIPIcs, Volume 335, 37th Euromicro Conference on Real-Time Systems (ECRTS 2025)

Abstract
Deep learning–based classifiers are widely used for perception in autonomous Cyber-Physical Systems (CPS’s). However, such classifiers rarely offer guarantees of perfect accuracy while being optimized for efficiency. To support safety-critical perception, ensembles of multiple different classifiers working in concert are typically used. Since CPS’s interact with the physical world continuously, it is not unreasonable to expect dependencies among successive inputs in a stream of sensor data. Prior work introduced a classification technique that leverages these inter-input dependencies to reduce the average time to successful classification using classifier ensembles. In this paper, we propose generalizations to this classification technique, both in the improved generation of classifier cascades and the modeling of temporal dependencies. We demonstrate, through theoretical analysis and numerical evaluation, that our approach achieves further reductions in average classification latency compared to the prior methods.
Cite as

Kunal Agrawal, Sanjoy Baruah, Zhishan Guo, Jing Li, Federico Reghenzani, Kecheng Yang, and Jinhao Zhao. Faster Classification of Time-Series Input Streams. In 37th Euromicro Conference on Real-Time Systems (ECRTS 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 335, pp. 13:1-13:22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{agrawal_et_al:LIPIcs.ECRTS.2025.13,
  author =	{Agrawal, Kunal and Baruah, Sanjoy and Guo, Zhishan and Li, Jing and Reghenzani, Federico and Yang, Kecheng and Zhao, Jinhao},
  title =	{{Faster Classification of Time-Series Input Streams}},
  booktitle =	{37th Euromicro Conference on Real-Time Systems (ECRTS 2025)},
  pages =	{13:1--13:22},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-377-5},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{335},
  editor =	{Mancuso, Renato},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ECRTS.2025.13},
  URN =		{urn:nbn:de:0030-drops-235919},
  doi =		{10.4230/LIPIcs.ECRTS.2025.13},
  annote =	{Keywords: Classification, Deep Learning, Sensor data streams, IDK classifiers}
}
Document
DOI: 10.4230/LIPIcs.ECRTS.2025.15
Analysis of EDF for Real-Time Multiprocessor Systems with Resource Sharing

Authors: Kunal Agrawal, Sanjoy Baruah, Jeremy T. Fineman, Alberto Marchetti-Spaccamela, and Jinhao Zhao

Published in: LIPIcs, Volume 335, 37th Euromicro Conference on Real-Time Systems (ECRTS 2025)

Abstract
The classic Earliest Deadline First (EDF) algorithm is widely studied and used due to its simplicity and strong theoretical performance, but has not been rigorously analyzed for systems where jobs may execute critical sections protected by shared locks. Analyzing such systems is often challenging due to unpredictable delays caused by contention. In this paper, we propose a straightforward generalization of EDF, called EDF-Block. In this generalization, the critical sections are executed non-preemptively, but scheduling and lock acquisition priorities are based on EDF. We establish lower bounds on the speed augmentation required for any non-clairvoyant scheduler (EDF-Block is an example of non-clairvoyant schedulers) and for EDF-Block, showing that EDF-Block requires at least 4.11× speed augmentation for jobs and 4× for tasks. We then provide an upper bound analysis, demonstrating that EDF-Block requires speedup of at most 6 to schedule all feasible job and task sets.
Cite as

Kunal Agrawal, Sanjoy Baruah, Jeremy T. Fineman, Alberto Marchetti-Spaccamela, and Jinhao Zhao. Analysis of EDF for Real-Time Multiprocessor Systems with Resource Sharing. In 37th Euromicro Conference on Real-Time Systems (ECRTS 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 335, pp. 15:1-15:26, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{agrawal_et_al:LIPIcs.ECRTS.2025.15,
  author =	{Agrawal, Kunal and Baruah, Sanjoy and Fineman, Jeremy T. and Marchetti-Spaccamela, Alberto and Zhao, Jinhao},
  title =	{{Analysis of EDF for Real-Time Multiprocessor Systems with Resource Sharing}},
  booktitle =	{37th Euromicro Conference on Real-Time Systems (ECRTS 2025)},
  pages =	{15:1--15:26},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-377-5},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{335},
  editor =	{Mancuso, Renato},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ECRTS.2025.15},
  URN =		{urn:nbn:de:0030-drops-235932},
  doi =		{10.4230/LIPIcs.ECRTS.2025.15},
  annote =	{Keywords: Real-Time Scheduling, Non-Clairvoyant Scheduling, EDF, Competitive Analysis, Shared Resources}
}
Document
Artifact
DOI: 10.4230/DARTS.11.1.4
Faster Classification of Time-Series Input Streams (Artifact)

Authors: Kunal Agrawal, Sanjoy Baruah, Zhishan Guo, Jing Li, Federico Reghenzani, Kecheng Yang, and Jinhao Zhao

Published in: DARTS, Volume 11, Issue 1, Special Issue of the 37th Euromicro Conference on Real-Time Systems (ECRTS 2025)

Abstract
Deep learning–based classifiers are widely used for perception in autonomous Cyber-Physical Systems (CPS’s). However, such classifiers rarely offer guarantees of perfect accuracy while being optimized for efficiency. To support safety-critical perception, ensembles of multiple different classifiers working in concert are typically used. Since CPS’s interact with the physical world continuously, it is not unreasonable to expect dependencies among successive inputs in a stream of sensor data. Prior work introduced a classification technique that leverages these inter-input dependencies to reduce the average time to successful classification using classifier ensembles. In this paper, we propose generalizations to this classification technique, both in the improved generation of classifier cascades and the modeling of temporal dependencies. We demonstrate, through theoretical analysis and numerical evaluation, that our approach achieves further reductions in average classification latency compared to the prior methods.
Cite as

Kunal Agrawal, Sanjoy Baruah, Zhishan Guo, Jing Li, Federico Reghenzani, Kecheng Yang, and Jinhao Zhao. Faster Classification of Time-Series Input Streams (Artifact). In Special Issue of the 37th Euromicro Conference on Real-Time Systems (ECRTS 2025). Dagstuhl Artifacts Series (DARTS), Volume 11, Issue 1, pp. 4:1-4:3, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@Article{agrawal_et_al:DARTS.11.1.4,
  author =	{Agrawal, Kunal and Baruah, Sanjoy and Guo, Zhishan and Li, Jing and Reghenzani, Federico and Yang, Kecheng and Zhao, Jinhao},
  title =	{{Faster Classification of Time-Series Input Streams (Artifact)}},
  pages =	{4:1--4:3},
  journal =	{Dagstuhl Artifacts Series},
  ISSN =	{2509-8195},
  year =	{2025},
  volume =	{11},
  number =	{1},
  editor =	{Agrawal, Kunal and Baruah, Sanjoy and Guo, Zhishan and Li, Jing and Reghenzani, Federico and Yang, Kecheng and Zhao, Jinhao},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DARTS.11.1.4},
  URN =		{urn:nbn:de:0030-drops-236057},
  doi =		{10.4230/DARTS.11.1.4},
  annote =	{Keywords: Classification, Deep Learning, Sensor data streams, IDK classifiers}
}
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