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Documents authored by Agrawal, Kunal

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}
}
Document
DOI: 10.4230/LIPIcs.ECRTS.2023.3
The Safe and Effective Use of Low-Assurance Predictions in Safety-Critical Systems

Authors: Kunal Agrawal, Sanjoy Baruah, Michael A. Bender, and Alberto Marchetti-Spaccamela

Published in: LIPIcs, Volume 262, 35th Euromicro Conference on Real-Time Systems (ECRTS 2023)

Abstract
The algorithm-design paradigm of algorithms using predictions is explored as a means of incorporating the computations of lower-assurance components (such as machine-learning based ones) into safety-critical systems that must have their correctness validated to very high levels of assurance. The paradigm is applied to two simple example applications that are relevant to the real-time systems community: energy-aware scheduling, and classification using ML-based classifiers in conjunction with more reliable but slower deterministic classifiers. It is shown how algorithms using predictions achieve much-improved performance when the low-assurance computations are correct, at a cost of no more than a slight performance degradation even when they turn out to be completely wrong.
Cite as

Kunal Agrawal, Sanjoy Baruah, Michael A. Bender, and Alberto Marchetti-Spaccamela. The Safe and Effective Use of Low-Assurance Predictions in Safety-Critical Systems. In 35th Euromicro Conference on Real-Time Systems (ECRTS 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 262, pp. 3:1-3:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{agrawal_et_al:LIPIcs.ECRTS.2023.3,
  author =	{Agrawal, Kunal and Baruah, Sanjoy and Bender, Michael A. and Marchetti-Spaccamela, Alberto},
  title =	{{The Safe and Effective Use of Low-Assurance Predictions in Safety-Critical Systems}},
  booktitle =	{35th Euromicro Conference on Real-Time Systems (ECRTS 2023)},
  pages =	{3:1--3:19},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-280-8},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{262},
  editor =	{Papadopoulos, Alessandro V.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ECRTS.2023.3},
  URN =		{urn:nbn:de:0030-drops-180323},
  doi =		{10.4230/LIPIcs.ECRTS.2023.3},
  annote =	{Keywords: Algorithms using predictions, robust scheduling, energy minimization, classification, on-line scheduling}
}
Document
DOI: 10.4230/LIPIcs.ECRTS.2020.7
The Safe and Effective Use of Learning-Enabled Components in Safety-Critical Systems

Authors: Kunal Agrawal, Sanjoy Baruah, and Alan Burns

Published in: LIPIcs, Volume 165, 32nd Euromicro Conference on Real-Time Systems (ECRTS 2020)

Abstract
Autonomous systems increasingly use components that incorporate machine learning and other AI-based techniques in order to achieve improved performance. The problem of assuring correctness in safety-critical systems that use such components is considered. A model is proposed in which components are characterized according to both their worst-case and their typical behaviors; it is argued that while safety must be assured under all circumstances, it is reasonable to be concerned with providing a high degree of performance for typical behaviors only. The problem of assuring safety while providing such improved performance is formulated as an optimization problem in which performance under typical circumstances is the objective function to be optimized while safety is a hard constraint that must be satisfied. Algorithmic techniques are applied to derive an optimal solution to this optimization problem. This optimal solution is compared with an alternative approach that optimizes for performance under worst-case conditions, as well as some common-sense heuristics, via simulation experiments on synthetically-generated workloads.
Cite as

Kunal Agrawal, Sanjoy Baruah, and Alan Burns. The Safe and Effective Use of Learning-Enabled Components in Safety-Critical Systems. In 32nd Euromicro Conference on Real-Time Systems (ECRTS 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 165, pp. 7:1-7:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

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@InProceedings{agrawal_et_al:LIPIcs.ECRTS.2020.7,
  author =	{Agrawal, Kunal and Baruah, Sanjoy and Burns, Alan},
  title =	{{The Safe and Effective Use of Learning-Enabled Components in Safety-Critical Systems}},
  booktitle =	{32nd Euromicro Conference on Real-Time Systems (ECRTS 2020)},
  pages =	{7:1--7:20},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-152-8},
  ISSN =	{1868-8969},
  year =	{2020},
  volume =	{165},
  editor =	{V\"{o}lp, Marcus},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ECRTS.2020.7},
  URN =		{urn:nbn:de:0030-drops-123704},
  doi =		{10.4230/LIPIcs.ECRTS.2020.7},
  annote =	{Keywords: Learning-enabled components (LECs), Safety-critical systems, Typical analysis, Performance optimization, Run-time monitoring}
}
Document
DOI: 10.4230/DagRep.9.3.1
Analysis, Design, and Control of Predictable Interconnected Systems (Dagstuhl Seminar 19101)

Authors: Kunal Agrawal, Enrico Bini, and Giovanni Stea

Published in: Dagstuhl Reports, Volume 9, Issue 3 (2019)

Abstract
We call "Interconnected Systems" any collection of systems distributed over a metric space whose behavior is influenced by its neighborhood. Examples of interconnected systems exist at very different scales: different cores over the same silicon, different sub-systems in vehicles, communicating nodes over either a physical (e.g., optical) network, or - more recently - virtualized network. Examples also exist in contexts which are not related to computing or communication. Smart Grids (of energy production, distribution, and consumption) and Intelligent Transportation Systems are just two notable examples. The common characteristic among all these examples is the presence of a spatially distributed demand of resources (energy, computing, communication bandwidth, etc.) which needs to be matched with a spatially distributed supply. Often times demands and availability of resources of different types (e.g., computing and link bandwidth in virtualized network environments) need to be matched simultaneously. Time predictability is a key requirement for above systems. Despite this, the strong market pressure has often led to ``quick and dirty'' best-effort solutions, which make it extremely challenging to predict the behavior of such systems. Research communities have developed formal theories for predictability which are specialized to each application domain or type of resource (e.g., schedulability analysis for real-time systems or network calculus for communication systems). However, the emerging application domains (virtualized networks, cyber-physical systems, etc.) clearly require a unified, holistic approach. By leveraging the expertise, vision and interactions of scientists that have addressed predictability in different areas, the proposed seminar aims at constructing a common ground for the theory supporting the analysis, the design, and the control of predictable interconnected systems.
Cite as

Kunal Agrawal, Enrico Bini, and Giovanni Stea. Analysis, Design, and Control of Predictable Interconnected Systems (Dagstuhl Seminar 19101). In Dagstuhl Reports, Volume 9, Issue 3, pp. 1-15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)

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@Article{agrawal_et_al:DagRep.9.3.1,
  author =	{Agrawal, Kunal and Bini, Enrico and Stea, Giovanni},
  title =	{{Analysis, Design, and Control of Predictable Interconnected Systems (Dagstuhl Seminar 19101)}},
  pages =	{1--15},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2019},
  volume =	{9},
  number =	{3},
  editor =	{Agrawal, Kunal and Bini, Enrico and Stea, Giovanni},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.9.3.1},
  URN =		{urn:nbn:de:0030-drops-112882},
  doi =		{10.4230/DagRep.9.3.1},
  annote =	{Keywords: distributed resource management, network calculus, real-time systems}
}
Document
DOI: 10.4230/LITES-v006-i001-a005
Elastic Scheduling for Parallel Real-Time Systems

Authors: James Orr, Chris Gill, Kunal Agrawal, Jing Li, and Sanjoy Baruah

Published in: LITES, Volume 6, Issue 1 (2019). Leibniz Transactions on Embedded Systems, Volume 6, Issue 1

Abstract
The elastic task model was introduced by Buttazzo et al.~in order to represent recurrent real-time workloads executing upon uniprocessor platforms that are somewhat flexible with regards to timing constraints.  In this work, we propose an extension of this model and apply it to represent recurrent real-time workloads that exhibit internal parallelism and are executed on multiprocessor platforms. In our proposed extension, the elasticity coefficient - the quantitative measure of a task's elasticity that was introduced in the model proposed by Buttazzo et al. - is interpreted in the same manner as in the original (sequential) model. Hence, system developers who are familiar with the elastic task model in the uniprocessor context may use our more general model as they had previously done, now for real-time tasks whose computational demands require them to utilize more than one processor.
Cite as

James Orr, Chris Gill, Kunal Agrawal, Jing Li, and Sanjoy Baruah. Elastic Scheduling for Parallel Real-Time Systems. In LITES, Volume 6, Issue 1 (2019). Leibniz Transactions on Embedded Systems, Volume 6, Issue 1, pp. 05:1-05:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)

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@Article{orr_et_al:LITES-v006-i001-a005,
  author =	{Orr, James and Gill, Chris and Agrawal, Kunal and Li, Jing and Baruah, Sanjoy},
  title =	{{Elastic Scheduling for Parallel Real-Time Systems}},
  journal =	{Leibniz Transactions on Embedded Systems},
  pages =	{05:1--05:14},
  ISSN =	{2199-2002},
  year =	{2019},
  volume =	{6},
  number =	{1},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LITES-v006-i001-a005},
  URN =		{urn:nbn:de:0030-drops-192819},
  doi =		{10.4230/LITES-v006-i001-a005},
  annote =	{Keywords: Parallel real-time tasks, multiprocessor federated scheduling, elasticity coefficient}
}
Document
DOI: 10.4230/LIPIcs.ECRTS.2018.5
A Measurement-Based Model for Parallel Real-Time Tasks

Authors: Kunal Agrawal and Sanjoy Baruah

Published in: LIPIcs, Volume 106, 30th Euromicro Conference on Real-Time Systems (ECRTS 2018)

Abstract
Under the federated paradigm of multiprocessor scheduling, a set of processors is reserved for the exclusive use of each real-time task. If tasks are characterized very conservatively (as is typical in safety-critical systems), it is likely that most invocations of the task will have computational demand far below the worst-case characterization, and could have been scheduled correctly upon far fewer processors than were assigned to it assuming the worst-case characterization of its run-time behavior. Provided we could safely determine during run-time when all the processors are going to be needed, for the rest of the time the unneeded processors could be idled in low-energy "sleep" mode, or used for executing non-real time work in the background. In this paper we propose a model for representing parallelizable real-time tasks in a manner that permits us to do so. Our model does not require us to have fine-grained knowledge of the internal structure of the code represented by the task; rather, it characterizes each task by a few parameters that are obtained by repeatedly executing the code under different conditions and measuring the run-times.
Cite as

Kunal Agrawal and Sanjoy Baruah. A Measurement-Based Model for Parallel Real-Time Tasks. In 30th Euromicro Conference on Real-Time Systems (ECRTS 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 106, pp. 5:1-5:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)

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@InProceedings{agrawal_et_al:LIPIcs.ECRTS.2018.5,
  author =	{Agrawal, Kunal and Baruah, Sanjoy},
  title =	{{A Measurement-Based Model for Parallel Real-Time Tasks}},
  booktitle =	{30th Euromicro Conference on Real-Time Systems (ECRTS 2018)},
  pages =	{5:1--5:19},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-075-0},
  ISSN =	{1868-8969},
  year =	{2018},
  volume =	{106},
  editor =	{Altmeyer, Sebastian},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ECRTS.2018.5},
  URN =		{urn:nbn:de:0030-drops-89999},
  doi =		{10.4230/LIPIcs.ECRTS.2018.5},
  annote =	{Keywords: multiprocessor federated scheduling, parallel tasks, work and span, mixed criticality}
}
Document
DOI: 10.4230/LIPIcs.ECRTS.2018.11
Intractability Issues in Mixed-Criticality Scheduling

Authors: Kunal Agrawal and Sanjoy Baruah

Published in: LIPIcs, Volume 106, 30th Euromicro Conference on Real-Time Systems (ECRTS 2018)

Abstract
In seeking to develop mixed-criticality scheduling algorithms, one encounters challenges arising from two sources. First, mixed-criticality scheduling is an inherently an on-line problem in that scheduling decisions must be made without access to all the information that is needed to make such decisions optimally - such information is only revealed over time. Second, many fundamental mixed-criticality schedulability analysis problems are computationally intractable - NP-hard in the strong sense - but we desire to solve these problems using algorithms with polynomial or pseudo-polynomial running time. While these two aspects of intractability are traditionally studied separately in the theoretical computer science literature, they have been considered in an integrated fashion in mixed-criticality scheduling theory. In this work we seek to separate out the effects of being inherently on-line, and being computationally intractable, on the overall intractability of mixed-criticality scheduling problems. Speedup factor is widely used as quantitative metric of the effectiveness of mixed-criticality scheduling algorithms; there has recently been a bit of a debate regarding the appropriateness of doing so. We provide here some additional perspective on this matter: we seek to better understand its appropriateness as well as its limitations in this regard by examining separately how the on-line nature of some mixed-criticality problems, and their computational complexity, contribute to the speedup factors of two widely-studied mixed-criticality scheduling algorithms.
Cite as

Kunal Agrawal and Sanjoy Baruah. Intractability Issues in Mixed-Criticality Scheduling. In 30th Euromicro Conference on Real-Time Systems (ECRTS 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 106, pp. 11:1-11:21, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)

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@InProceedings{agrawal_et_al:LIPIcs.ECRTS.2018.11,
  author =	{Agrawal, Kunal and Baruah, Sanjoy},
  title =	{{Intractability Issues in Mixed-Criticality Scheduling}},
  booktitle =	{30th Euromicro Conference on Real-Time Systems (ECRTS 2018)},
  pages =	{11:1--11:21},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-075-0},
  ISSN =	{1868-8969},
  year =	{2018},
  volume =	{106},
  editor =	{Altmeyer, Sebastian},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ECRTS.2018.11},
  URN =		{urn:nbn:de:0030-drops-89925},
  doi =		{10.4230/LIPIcs.ECRTS.2018.11},
  annote =	{Keywords: mixed-criticality scheduling, speedup factor, competitive ratio, approximation ratio, NP-completeness results, sporadic tasks}
}
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