8 Search Results for "Li, Jing"

Document
Short Paper
DOI: 10.4230/LIPIcs.GIScience.2023.86
A Comparison of Global and Local Statistical and Machine Learning Techniques in Estimating Flash Flood Susceptibility (Short Paper)

Authors: Jing Yao, Ziqi Li, Xiaoxiang Zhang, Changjun Liu, and Liliang Ren

Published in: LIPIcs, Volume 277, 12th International Conference on Geographic Information Science (GIScience 2023)

Abstract
Flash floods, as a type of devastating natural disasters, can cause significant damage to infrastructure, agriculture, and people’s livelihoods. Mapping flash flood susceptibility has long been an effective measure to help with the development of flash flood risk reduction and management strategies. Recent studies have shown that machine learning (ML) techniques perform better than traditional statistical and process-based models in estimating flash flood susceptibility. However, a major limitation of standard ML models is that they ignore the local geographic context where flash floods occur. To address this limitation, we developed a local Geographically Weighted Random Forest (GWRF) model and compared its performance against other global and local statistical and ML alternatives using an empirical flash floods model of Jiangxi Province, China.
Cite as

Jing Yao, Ziqi Li, Xiaoxiang Zhang, Changjun Liu, and Liliang Ren. A Comparison of Global and Local Statistical and Machine Learning Techniques in Estimating Flash Flood Susceptibility (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 86:1-86:6, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2023)

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@InProceedings{yao_et_al:LIPIcs.GIScience.2023.86,
  author =	{Yao, Jing and Li, Ziqi and Zhang, Xiaoxiang and Liu, Changjun and Ren, Liliang},
  title =	{{A Comparison of Global and Local Statistical and Machine Learning Techniques in Estimating Flash Flood Susceptibility}},
  booktitle =	{12th International Conference on Geographic Information Science (GIScience 2023)},
  pages =	{86:1--86:6},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-288-4},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{277},
  editor =	{Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.86},
  URN =		{urn:nbn:de:0030-drops-189815},
  doi =		{10.4230/LIPIcs.GIScience.2023.86},
  annote =	{Keywords: Machine Learning, Spatial Statistics, Flash floods, Susceptibility}
}
Document
DOI: 10.4230/LIPIcs.ECRTS.2023.8
Precise Scheduling of DAG Tasks with Dynamic Power Management

Authors: Ashikahmed Bhuiyan, Mohammad Pivezhandi, Zhishan Guo, Jing Li, Venkata Prashant Modekurthy, and Abusayeed Saifullah

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

Abstract
The rigid timing requirement of real-time applications biases the analysis to focus on the worst-case performances. Such a focus cannot provide enough information to optimize the system’s typical resource and energy consumption. In this work, we study the real-time scheduling of parallel tasks on a multi-speed heterogeneous platform while minimizing their typical-case CPU energy consumption. Dynamic power management (DPM) policy is integrated to determine the minimum number of cores required for each task while guaranteeing worst-case execution requirements (under all circumstances). A Hungarian Algorithm-based task partitioning technique is proposed for clustered multi-core platforms, where all cores within the same cluster run at the same speed at any time, while different clusters may run at different speeds. To our knowledge, this is the first work aiming to minimize typical-case CPU energy consumption (while ensuring the worst-case timing correctness for all tasks under any execution condition) through DPM for parallel tasks in a clustered platform. We demonstrate the effectiveness of the proposed approach with existing power management techniques using experimental results and simulations. The experimental results conducted on the Intel Xeon 2680 v3 12-core platform show around 7%-30% additional energy savings.
Cite as

Ashikahmed Bhuiyan, Mohammad Pivezhandi, Zhishan Guo, Jing Li, Venkata Prashant Modekurthy, and Abusayeed Saifullah. Precise Scheduling of DAG Tasks with Dynamic Power Management. In 35th Euromicro Conference on Real-Time Systems (ECRTS 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 262, pp. 8:1-8:24, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2023)

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@InProceedings{bhuiyan_et_al:LIPIcs.ECRTS.2023.8,
  author =	{Bhuiyan, Ashikahmed and Pivezhandi, Mohammad and Guo, Zhishan and Li, Jing and Modekurthy, Venkata Prashant and Saifullah, Abusayeed},
  title =	{{Precise Scheduling of DAG Tasks with Dynamic Power Management}},
  booktitle =	{35th Euromicro Conference on Real-Time Systems (ECRTS 2023)},
  pages =	{8:1--8:24},
  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.2023.8},
  URN =		{urn:nbn:de:0030-drops-180372},
  doi =		{10.4230/LIPIcs.ECRTS.2023.8},
  annote =	{Keywords: Parallel task, mixed-criticality scheduling, energy minimization, dynamic power management, cluster-based platform}
}
Document
DOI: 10.4230/LITES.8.1.3
HW-Flow: A Multi-Abstraction Level HW-CNN Codesign Pruning Methodology

Authors: Manoj-Rohit Vemparala, Nael Fasfous, Alexander Frickenstein, Emanuele Valpreda, Manfredi Camalleri, Qi Zhao, Christian Unger, Naveen-Shankar Nagaraja, Maurizio Martina, and Walter Stechele

Published in: LITES, Volume 8, Issue 1 (2022): Special Issue on Embedded Systems for Computer Vision. Leibniz Transactions on Embedded Systems, Volume 8, Issue 1

Abstract
Convolutional neural networks (CNNs) have produced unprecedented accuracy for many computer vision problems in the recent past. In power and compute-constrained embedded platforms, deploying modern CNNs can present many challenges. Most CNN architectures do not run in real-time due to the high number of computational operations involved during the inference phase. This emphasizes the role of CNN optimization techniques in early design space exploration. To estimate their efficacy in satisfying the target constraints, existing techniques are either hardware (HW) agnostic, pseudo-HW-aware by considering parameter and operation counts, or HW-aware through inflexible hardware-in-the-loop (HIL) setups. In this work, we introduce HW-Flow, a framework for optimizing and exploring CNN models based on three levels of hardware abstraction: Coarse, Mid and Fine. Through these levels, CNN design and optimization can be iteratively refined towards efficient execution on the target hardware platform. We present HW-Flow in the context of CNN pruning by augmenting a reinforcement learning agent with key metrics to understand the influence of its pruning actions on the inference hardware. With 2× reduction in energy and latency, we prune ResNet56, ResNet50, and DeepLabv3 with minimal accuracy degradation on the CIFAR-10, ImageNet, and CityScapes datasets, respectively.
Cite as

Manoj-Rohit Vemparala, Nael Fasfous, Alexander Frickenstein, Emanuele Valpreda, Manfredi Camalleri, Qi Zhao, Christian Unger, Naveen-Shankar Nagaraja, Maurizio Martina, and Walter Stechele. HW-Flow: A Multi-Abstraction Level HW-CNN Codesign Pruning Methodology. In LITES, Volume 8, Issue 1 (2022): Special Issue on Embedded Systems for Computer Vision. Leibniz Transactions on Embedded Systems, Volume 8, Issue 1, pp. 03:1-03:30, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2022)

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@Article{vemparala_et_al:LITES.8.1.3,
  author =	{Vemparala, Manoj-Rohit and Fasfous, Nael and Frickenstein, Alexander and Valpreda, Emanuele and Camalleri, Manfredi and Zhao, Qi and Unger, Christian and Nagaraja, Naveen-Shankar and Martina, Maurizio and Stechele, Walter},
  title =	{{HW-Flow: A Multi-Abstraction Level HW-CNN Codesign Pruning Methodology}},
  booktitle =	{LITES, Volume 8, Issue 1 (2022): Special Issue on Embedded Systems for Computer Vision},
  pages =	{03:1--03:30},
  journal =	{Leibniz Transactions on Embedded Systems},
  ISSN =	{2199-2002},
  year =	{2022},
  volume =	{8},
  number =	{1},
  editor =	{Vemparala, Manoj-Rohit and Fasfous, Nael and Frickenstein, Alexander and Valpreda, Emanuele and Camalleri, Manfredi and Zhao, Qi and Unger, Christian and Nagaraja, Naveen-Shankar and Martina, Maurizio and Stechele, Walter},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LITES.8.1.3},
  doi =		{10.4230/LITES.8.1.3},
  annote =	{Keywords: Convolutional Neural Networks, Optimization, Hardware Modeling, Pruning}
}
Document
APPROX
DOI: 10.4230/LIPIcs.APPROX/RANDOM.2020.48
Maximizing Throughput in Flow Shop Real-Time Scheduling

Authors: Lior Ben Yamin, Jing Li, Kanthi Sarpatwar, Baruch Schieber, and Hadas Shachnai

Published in: LIPIcs, Volume 176, Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2020)

Abstract
We consider scheduling real-time jobs in the classic flow shop model. The input is a set of n jobs, each consisting of m segments to be processed on m machines in the specified order, such that segment I_i of a job can start processing on machine M_i only after segment I_{i-1} of the same job completed processing on machine M_{i-1}, for 2 ≤ i ≤ m. Each job also has a release time, a due date, and a weight. The objective is to maximize the throughput (or, profit) of the n jobs, i.e., to find a subset of the jobs that have the maximum total weight and can complete processing on the m machines within their time windows. This problem has numerous real-life applications ranging from manufacturing to cloud and embedded computing platforms, already in the special case where m = 2. Previous work in the flow shop model has focused on makespan, flow time, or tardiness objectives. However, little is known for the flow shop model in the real-time setting. In this work, we give the first nontrivial results for this problem and present a pseudo-polynomial time (2m+1)-approximation algorithm for the problem on m ≥ 2 machines, where m is a constant. This ratio is essentially tight due to a hardness result of Ω(m/(log m)) for the approximation ratio. We further give a polynomial-time algorithm for the two-machine case, with an approximation ratio of (9+ε) where ε = O(1/n). We obtain better bounds for some restricted subclasses of inputs with two machines. To the best of our knowledge, this fundamental problem of throughput maximization in the flow shop scheduling model is studied here for the first time.
Cite as

Lior Ben Yamin, Jing Li, Kanthi Sarpatwar, Baruch Schieber, and Hadas Shachnai. Maximizing Throughput in Flow Shop Real-Time Scheduling. In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 176, pp. 48:1-48:18, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2020)

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@InProceedings{benyamin_et_al:LIPIcs.APPROX/RANDOM.2020.48,
  author =	{Ben Yamin, Lior and Li, Jing and Sarpatwar, Kanthi and Schieber, Baruch and Shachnai, Hadas},
  title =	{{Maximizing Throughput in Flow Shop Real-Time Scheduling}},
  booktitle =	{Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2020)},
  pages =	{48:1--48:18},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-164-1},
  ISSN =	{1868-8969},
  year =	{2020},
  volume =	{176},
  editor =	{Byrka, Jaros{\l}aw and Meka, Raghu},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.APPROX/RANDOM.2020.48},
  URN =		{urn:nbn:de:0030-drops-126510},
  doi =		{10.4230/LIPIcs.APPROX/RANDOM.2020.48},
  annote =	{Keywords: Flow shop, real-time scheduling, throughput maximization, approximation algorithms}
}
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}},
  booktitle =	{LITES, Volume 6, Issue 1 (2019)},
  pages =	{05:1--05:14},
  journal =	{Leibniz Transactions on Embedded Systems},
  ISSN =	{2199-2002},
  year =	{2019},
  volume =	{6},
  number =	{1},
  editor =	{Orr, James and Gill, Chris and Agrawal, Kunal and Li, Jing and Baruah, Sanjoy},
  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},
  doi =		{10.4230/LITES-v006-i001-a005},
  annote =	{Keywords: Parallel real-time tasks, multiprocessor federated scheduling, elasticity coefficient}
}
Document
Brief Announcement
DOI: 10.4230/LIPIcs.ICALP.2018.108
Brief Announcement: Bayesian Auctions with Efficient Queries

Authors: Jing Chen, Bo Li, Yingkai Li, and Pinyan Lu

Published in: LIPIcs, Volume 107, 45th International Colloquium on Automata, Languages, and Programming (ICALP 2018)

Abstract
Generating good revenue is one of the most important problems in Bayesian auction design, and many (approximately) optimal dominant-strategy incentive compatible (DSIC) Bayesian mechanisms have been constructed for various auction settings. However, most existing studies do not consider the complexity for the seller to carry out the mechanism. It is assumed that the seller knows "each single bit" of the distributions and is able to optimize perfectly based on the entire distributions. Unfortunately this is a strong assumption and may not hold in reality: for example, when the value distributions have exponentially large supports or do not have succinct representations. In this work we consider, for the first time, the query complexity of Bayesian mechanisms. We only allow the seller to have limited oracle accesses to the players' value distributions, via quantile queries and value queries. For a large class of auction settings, we prove logarithmic lower-bounds for the query complexity for any DSIC Bayesian mechanism to be of any constant approximation to the optimal revenue. For single-item auctions and multi-item auctions with unit-demand or additive valuation functions, we prove tight upper-bounds via efficient query schemes, without requiring the distributions to be regular or have monotone hazard rate. Thus, in those auction settings the seller needs to access much less than the full distributions in order to achieve approximately optimal revenue.
Cite as

Jing Chen, Bo Li, Yingkai Li, and Pinyan Lu. Brief Announcement: Bayesian Auctions with Efficient Queries. In 45th International Colloquium on Automata, Languages, and Programming (ICALP 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 107, pp. 108:1-108:4, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2018)

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@InProceedings{chen_et_al:LIPIcs.ICALP.2018.108,
  author =	{Chen, Jing and Li, Bo and Li, Yingkai and Lu, Pinyan},
  title =	{{Brief Announcement: Bayesian Auctions with Efficient Queries}},
  booktitle =	{45th International Colloquium on Automata, Languages, and Programming (ICALP 2018)},
  pages =	{108:1--108:4},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-076-7},
  ISSN =	{1868-8969},
  year =	{2018},
  volume =	{107},
  editor =	{Chatzigiannakis, Ioannis and Kaklamanis, Christos and Marx, D\'{a}niel and Sannella, Donald},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ICALP.2018.108},
  URN =		{urn:nbn:de:0030-drops-91124},
  doi =		{10.4230/LIPIcs.ICALP.2018.108},
  annote =	{Keywords: The complexity of Bayesian mechanisms, quantile queries, value queries}
}
Document
DOI: 10.4230/LIPIcs.ICALP.2017.11
Efficient Approximations for the Online Dispersion Problem

Authors: Jing Chen, Bo Li, and Yingkai Li

Published in: LIPIcs, Volume 80, 44th International Colloquium on Automata, Languages, and Programming (ICALP 2017)

Abstract
The dispersion problem has been widely studied in computational geometry and facility location, and is closely related to the packing problem. The goal is to locate n points (e.g., facilities or persons) in a k-dimensional polytope, so that they are far away from each other and from the boundary of the polytope. In many real-world scenarios however, the points arrive and depart at different times, and decisions must be made without knowing future events. Therefore we study, for the first time in the literature, the online dispersion problem in Euclidean space. There are two natural objectives when time is involved: the all-time worst-case (ATWC) problem tries to maximize the minimum distance that ever appears at any time; and the cumulative distance (CD) problem tries to maximize the integral of the minimum distance throughout the whole time interval. Interestingly, the online problems are highly non-trivial even on a segment. For cumulative distance, this remains the case even when the problem is time-dependent but offline, with all the arriving and departure times given in advance. For the online ATWC problem on a segment, we construct a deterministic polynomial-time algorithm which is (2ln2+epsilon)-competitive, where epsilon>0 can be arbitrarily small and the algorithm's running time is polynomial in 1/epsilon. We show this algorithm is actually optimal. For the same problem in a square, we provide a 1.591-competitive algorithm and a 1.183 lower-bound. Furthermore, for arbitrary k-dimensional polytopes with k>=2, we provide a 2/(1-epsilon)-competitive algorithm and a 7/6 lower-bound. All our lower-bounds come from the structure of the online problems and hold even when computational complexity is not a concern. Interestingly, for the offline CD problem in arbitrary k-dimensional polytopes, we provide a polynomial-time black-box reduction to the online ATWC problem, and the resulting competitive ratio increases by a factor of at most 2. Our techniques also apply to online dispersion problems with different boundary conditions.
Cite as

Jing Chen, Bo Li, and Yingkai Li. Efficient Approximations for the Online Dispersion Problem. In 44th International Colloquium on Automata, Languages, and Programming (ICALP 2017). Leibniz International Proceedings in Informatics (LIPIcs), Volume 80, pp. 11:1-11:15, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2017)

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@InProceedings{chen_et_al:LIPIcs.ICALP.2017.11,
  author =	{Chen, Jing and Li, Bo and Li, Yingkai},
  title =	{{Efficient Approximations for the Online Dispersion Problem}},
  booktitle =	{44th International Colloquium on Automata, Languages, and Programming (ICALP 2017)},
  pages =	{11:1--11:15},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-041-5},
  ISSN =	{1868-8969},
  year =	{2017},
  volume =	{80},
  editor =	{Chatzigiannakis, Ioannis and Indyk, Piotr and Kuhn, Fabian and Muscholl, Anca},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ICALP.2017.11},
  URN =		{urn:nbn:de:0030-drops-74002},
  doi =		{10.4230/LIPIcs.ICALP.2017.11},
  annote =	{Keywords: dispersion, online algorithms, geometric optimization, packing, competitive algorithms}
}
Document
DOI: 10.4230/LITES-v003-i001-a004
Optimal Scheduling of Periodic Gang Tasks

Authors: Joël Goossens and Pascal Richard

Published in: LITES, Volume 3, Issue 1 (2016). Leibniz Transactions on Embedded Systems, Volume 3, Issue 1

Abstract
The gang scheduling of parallel implicit-deadline periodic task systems upon identical multiprocessor platforms is considered. In this scheduling problem, parallel tasks use several processors simultaneously. We propose two DPFAIR (deadline partitioning) algorithms that schedule all jobs in every interval of time delimited by two subsequent deadlines. These algorithms define a static schedule pattern that is stretched at run-time in every interval of the DPFAIR schedule. The first algorithm is based on linear programming and is the first one to be proved  optimal for the considered gang scheduling problem. Furthermore, it runs in polynomial time for a fixed number m of processors and an efficient implementation is fully detailed. The second algorithm is an approximation algorithm based on a fixed-priority rule that is competitive under resource augmentation analysis in order to compute an optimal schedule pattern. Precisely, its speedup factor is bounded by (2-1/m). Both algorithms are also evaluated through intensive numerical experiments.
Cite as

Joël Goossens and Pascal Richard. Optimal Scheduling of Periodic Gang Tasks. In LITES, Volume 3, Issue 1 (2016). Leibniz Transactions on Embedded Systems, Volume 3, Issue 1, pp. 04:1-04:18, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2016)

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@Article{goossens_et_al:LITES-v003-i001-a004,
  author =	{Goossens, Jo\"{e}l and Richard, Pascal},
  title =	{{Optimal Scheduling of Periodic Gang Tasks}},
  booktitle =	{LITES, Volume 3, Issue 1 (2016)},
  pages =	{04:1--04:18},
  journal =	{Leibniz Transactions on Embedded Systems},
  ISSN =	{2199-2002},
  year =	{2016},
  volume =	{3},
  number =	{1},
  editor =	{Goossens, Jo\"{e}l and Richard, Pascal},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LITES-v003-i001-a004},
  doi =		{10.4230/LITES-v003-i001-a004},
  annote =	{Keywords: Real-time systems, Scheduling, Parallel tasks}
}
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