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Documents authored by Megow, Nicole

Document
DOI: 10.4230/DagRep.13.2.1
Scheduling (Dagstuhl Seminar 23061)

Authors: Nicole Megow, Benjamin J. Moseley, David Shmoys, Ola Svensson, Sergei Vassilvitskii, and Jens Schlöter

Published in: Dagstuhl Reports, Volume 13, Issue 2 (2023)

Abstract
This report documents the program and the outcomes of Dagstuhl Seminar 23061 "Scheduling". The seminar focused on the emerging models for beyond-worst case algorithm design, in particular, recent approaches that incorporate learning. This includes models for the integration of learning into algorithm design that have been proposed recently and that have already demonstrated advances in the state-of-art for various scheduling applications: (i) scheduling with error-prone learned predictions, (ii) data-driven algorithm design, and (iii) stochastic and Bayesian learning in scheduling.
Cite as

Nicole Megow, Benjamin J. Moseley, David Shmoys, Ola Svensson, Sergei Vassilvitskii, and Jens Schlöter. Scheduling (Dagstuhl Seminar 23061). In Dagstuhl Reports, Volume 13, Issue 2, pp. 1-19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@Article{megow_et_al:DagRep.13.2.1,
  author =	{Megow, Nicole and Moseley, Benjamin J. and Shmoys, David and Svensson, Ola and Vassilvitskii, Sergei and Schl\"{o}ter, Jens},
  title =	{{Scheduling (Dagstuhl Seminar 23061)}},
  pages =	{1--19},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2023},
  volume =	{13},
  number =	{2},
  editor =	{Megow, Nicole and Moseley, Benjamin J. and Shmoys, David and Svensson, Ola and Vassilvitskii, Sergei and Schl\"{o}ter, Jens},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagRep.13.2.1},
  URN =		{urn:nbn:de:0030-drops-191789},
  doi =		{10.4230/DagRep.13.2.1},
  annote =	{Keywords: scheduling, mathematical optimization, approximation algorithms, learning methods, uncertainty}
}
Document
Complete Volume
DOI: 10.4230/LIPIcs.APPROX/RANDOM.2023
LIPIcs, Volume 275, APPROX/RANDOM 2023, Complete Volume

Authors: Nicole Megow and Adam Smith

Published in: LIPIcs, Volume 275, Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2023)

Abstract
LIPIcs, Volume 275, APPROX/RANDOM 2023, Complete Volume
Cite as

Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 275, pp. 1-1304, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@Proceedings{megow_et_al:LIPIcs.APPROX/RANDOM.2023,
  title =	{{LIPIcs, Volume 275, APPROX/RANDOM 2023, Complete Volume}},
  booktitle =	{Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2023)},
  pages =	{1--1304},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-296-9},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{275},
  editor =	{Megow, Nicole and Smith, Adam},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.APPROX/RANDOM.2023},
  URN =		{urn:nbn:de:0030-drops-188246},
  doi =		{10.4230/LIPIcs.APPROX/RANDOM.2023},
  annote =	{Keywords: LIPIcs, Volume 275, APPROX/RANDOM 2023, Complete Volume}
}
Document
Front Matter
DOI: 10.4230/LIPIcs.APPROX/RANDOM.2023.0
Front Matter, Table of Contents, Preface, Conference Organization

Authors: Nicole Megow and Adam Smith

Published in: LIPIcs, Volume 275, Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2023)

Abstract
Front Matter, Table of Contents, Preface, Conference Organization
Cite as

Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 275, pp. 0:i-0:xxiv, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{megow_et_al:LIPIcs.APPROX/RANDOM.2023.0,
  author =	{Megow, Nicole and Smith, Adam},
  title =	{{Front Matter, Table of Contents, Preface, Conference Organization}},
  booktitle =	{Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2023)},
  pages =	{0:i--0:xxiv},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-296-9},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{275},
  editor =	{Megow, Nicole and Smith, Adam},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.APPROX/RANDOM.2023.0},
  URN =		{urn:nbn:de:0030-drops-188254},
  doi =		{10.4230/LIPIcs.APPROX/RANDOM.2023.0},
  annote =	{Keywords: Front Matter, Table of Contents, Preface, Conference Organization}
}
Document
Track A: Algorithms, Complexity and Games
DOI: 10.4230/LIPIcs.ICALP.2023.65
Matching Augmentation via Simultaneous Contractions

Authors: Mohit Garg, Felix Hommelsheim, and Nicole Megow

Published in: LIPIcs, Volume 261, 50th International Colloquium on Automata, Languages, and Programming (ICALP 2023)

Abstract
We consider the matching augmentation problem (MAP), where a matching of a graph needs to be extended into a 2-edge-connected spanning subgraph by adding the minimum number of edges to it. We present a polynomial-time algorithm with an approximation ratio of 13/8 = 1.625 improving upon an earlier 5/3-approximation. The improvement builds on a new α-approximation preserving reduction for any α ≥ 3/2 from arbitrary MAP instances to well-structured instances that do not contain certain forbidden structures like parallel edges, small separators, and contractible subgraphs. We further introduce, as key ingredients, the technique of repeated simultaneous contractions and provide improved lower bounds for instances that cannot be contracted.
Cite as

Mohit Garg, Felix Hommelsheim, and Nicole Megow. Matching Augmentation via Simultaneous Contractions. In 50th International Colloquium on Automata, Languages, and Programming (ICALP 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 261, pp. 65:1-65:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{garg_et_al:LIPIcs.ICALP.2023.65,
  author =	{Garg, Mohit and Hommelsheim, Felix and Megow, Nicole},
  title =	{{Matching Augmentation via Simultaneous Contractions}},
  booktitle =	{50th International Colloquium on Automata, Languages, and Programming (ICALP 2023)},
  pages =	{65:1--65:17},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-278-5},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{261},
  editor =	{Etessami, Kousha and Feige, Uriel and Puppis, Gabriele},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.ICALP.2023.65},
  URN =		{urn:nbn:de:0030-drops-181176},
  doi =		{10.4230/LIPIcs.ICALP.2023.65},
  annote =	{Keywords: matching augmentation, approximation algorithms, 2-edge-connectivity}
}
Document
DOI: 10.4230/LIPIcs.ESA.2022.49
Learning-Augmented Query Policies for Minimum Spanning Tree with Uncertainty

Authors: Thomas Erlebach, Murilo Santos de Lima, Nicole Megow, and Jens Schlöter

Published in: LIPIcs, Volume 244, 30th Annual European Symposium on Algorithms (ESA 2022)

Abstract
We study how to utilize (possibly erroneous) predictions in a model for computing under uncertainty in which an algorithm can query unknown data. Our aim is to minimize the number of queries needed to solve the minimum spanning tree problem, a fundamental combinatorial optimization problem that has been central also to the research area of explorable uncertainty. For all integral γ ≥ 2, we present algorithms that are γ-robust and (1+1/γ)-consistent, meaning that they use at most γOPT queries if the predictions are arbitrarily wrong and at most (1+1/γ)OPT queries if the predictions are correct, where OPT is the optimal number of queries for the given instance. Moreover, we show that this trade-off is best possible. Furthermore, we argue that a suitably defined hop distance is a useful measure for the amount of prediction error and design algorithms with performance guarantees that degrade smoothly with the hop distance. We also show that the predictions are PAC-learnable in our model. Our results demonstrate that untrusted predictions can circumvent the known lower bound of 2, without any degradation of the worst-case ratio. To obtain our results, we provide new structural insights for the minimum spanning tree problem that might be useful in the context of query-based algorithms regardless of predictions. In particular, we generalize the concept of witness sets - the key to lower-bounding the optimum - by proposing novel global witness set structures and completely new ways of adaptively using those.
Cite as

Thomas Erlebach, Murilo Santos de Lima, Nicole Megow, and Jens Schlöter. Learning-Augmented Query Policies for Minimum Spanning Tree with Uncertainty. In 30th Annual European Symposium on Algorithms (ESA 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 244, pp. 49:1-49:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{erlebach_et_al:LIPIcs.ESA.2022.49,
  author =	{Erlebach, Thomas and de Lima, Murilo Santos and Megow, Nicole and Schl\"{o}ter, Jens},
  title =	{{Learning-Augmented Query Policies for Minimum Spanning Tree with Uncertainty}},
  booktitle =	{30th Annual European Symposium on Algorithms (ESA 2022)},
  pages =	{49:1--49:18},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-247-1},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{244},
  editor =	{Chechik, Shiri and Navarro, Gonzalo and Rotenberg, Eva and Herman, Grzegorz},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.ESA.2022.49},
  URN =		{urn:nbn:de:0030-drops-169872},
  doi =		{10.4230/LIPIcs.ESA.2022.49},
  annote =	{Keywords: explorable uncertainty, queries, untrusted predictions}
}
Document
DOI: 10.4230/LIPIcs.ITCS.2022.99
Double Coverage with Machine-Learned Advice

Authors: Alexander Lindermayr, Nicole Megow, and Bertrand Simon

Published in: LIPIcs, Volume 215, 13th Innovations in Theoretical Computer Science Conference (ITCS 2022)

Abstract
We study the fundamental online k-server problem in a learning-augmented setting. While in the traditional online model, an algorithm has no information about the request sequence, we assume that there is given some advice (e.g. machine-learned predictions) on an algorithm’s decision. There is, however, no guarantee on the quality of the prediction and it might be far from being correct. Our main result is a learning-augmented variation of the well-known Double Coverage algorithm for k-server on the line (Chrobak et al., SIDMA 1991) in which we integrate predictions as well as our trust into their quality. We give an error-dependent competitive ratio, which is a function of a user-defined confidence parameter, and which interpolates smoothly between an optimal consistency, the performance in case that all predictions are correct, and the best-possible robustness regardless of the prediction quality. When given good predictions, we improve upon known lower bounds for online algorithms without advice. We further show that our algorithm achieves for any k an almost optimal consistency-robustness tradeoff, within a class of deterministic algorithms respecting local and memoryless properties. Our algorithm outperforms a previously proposed (more general) learning-augmented algorithm. It is remarkable that the previous algorithm crucially exploits memory, whereas our algorithm is memoryless. Finally, we demonstrate in experiments the practicability and the superior performance of our algorithm on real-world data.
Cite as

Alexander Lindermayr, Nicole Megow, and Bertrand Simon. Double Coverage with Machine-Learned Advice. In 13th Innovations in Theoretical Computer Science Conference (ITCS 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 215, pp. 99:1-99:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{lindermayr_et_al:LIPIcs.ITCS.2022.99,
  author =	{Lindermayr, Alexander and Megow, Nicole and Simon, Bertrand},
  title =	{{Double Coverage with Machine-Learned Advice}},
  booktitle =	{13th Innovations in Theoretical Computer Science Conference (ITCS 2022)},
  pages =	{99:1--99:18},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-217-4},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{215},
  editor =	{Braverman, Mark},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.ITCS.2022.99},
  URN =		{urn:nbn:de:0030-drops-156954},
  doi =		{10.4230/LIPIcs.ITCS.2022.99},
  annote =	{Keywords: online k-server problem, competitive analysis, learning-augmented algorithms, untrusted predictions, consistency, robustness}
}
Document
DOI: 10.4230/LIPIcs.FSTTCS.2021.18
Fully Dynamic Algorithms for Knapsack Problems with Polylogarithmic Update Time

Authors: Franziska Eberle, Nicole Megow, Lukas Nölke, Bertrand Simon, and Andreas Wiese

Published in: LIPIcs, Volume 213, 41st IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science (FSTTCS 2021)

Abstract
Knapsack problems are among the most fundamental problems in optimization. In the Multiple Knapsack problem, we are given multiple knapsacks with different capacities and items with values and sizes. The task is to find a subset of items of maximum total value that can be packed into the knapsacks without exceeding the capacities. We investigate this problem and special cases thereof in the context of dynamic algorithms and design data structures that efficiently maintain near-optimal knapsack solutions for dynamically changing input. More precisely, we handle the arrival and departure of individual items or knapsacks during the execution of the algorithm with worst-case update time polylogarithmic in the number of items. As the optimal and any approximate solution may change drastically, we maintain implicit solutions and support polylogarithmic time query operations that can return the computed solution value and the packing of any given item. While dynamic algorithms are well-studied in the context of graph problems, there is hardly any work on packing problems (and generally much less on non-graph problems). Motivated by the theoretical interest in knapsack problems and their practical relevance, our work bridges this gap.
Cite as

Franziska Eberle, Nicole Megow, Lukas Nölke, Bertrand Simon, and Andreas Wiese. Fully Dynamic Algorithms for Knapsack Problems with Polylogarithmic Update Time. In 41st IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science (FSTTCS 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 213, pp. 18:1-18:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{eberle_et_al:LIPIcs.FSTTCS.2021.18,
  author =	{Eberle, Franziska and Megow, Nicole and N\"{o}lke, Lukas and Simon, Bertrand and Wiese, Andreas},
  title =	{{Fully Dynamic Algorithms for Knapsack Problems with Polylogarithmic Update Time}},
  booktitle =	{41st IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science (FSTTCS 2021)},
  pages =	{18:1--18:17},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-215-0},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{213},
  editor =	{Boja\'{n}czyk, Miko{\l}aj and Chekuri, Chandra},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.FSTTCS.2021.18},
  URN =		{urn:nbn:de:0030-drops-155297},
  doi =		{10.4230/LIPIcs.FSTTCS.2021.18},
  annote =	{Keywords: Fully dynamic algorithms, knapsack problem, approximation schemes}
}
Document
DOI: 10.4230/LIPIcs.ESA.2021.10
Orienting (Hyper)graphs Under Explorable Stochastic Uncertainty

Authors: Evripidis Bampis, Christoph Dürr, Thomas Erlebach, Murilo Santos de Lima, Nicole Megow, and Jens Schlöter

Published in: LIPIcs, Volume 204, 29th Annual European Symposium on Algorithms (ESA 2021)

Abstract
Given a hypergraph with uncertain node weights following known probability distributions, we study the problem of querying as few nodes as possible until the identity of a node with minimum weight can be determined for each hyperedge. Querying a node has a cost and reveals the precise weight of the node, drawn from the given probability distribution. Using competitive analysis, we compare the expected query cost of an algorithm with the expected cost of an optimal query set for the given instance. For the general case, we give a polynomial-time f(α)-competitive algorithm, where f(α) ∈ [1.618+ε,2] depends on the approximation ratio α for an underlying vertex cover problem. We also show that no algorithm using a similar approach can be better than 1.5-competitive. Furthermore, we give polynomial-time 4/3-competitive algorithms for bipartite graphs with arbitrary query costs and for hypergraphs with a single hyperedge and uniform query costs, with matching lower bounds.
Cite as

Evripidis Bampis, Christoph Dürr, Thomas Erlebach, Murilo Santos de Lima, Nicole Megow, and Jens Schlöter. Orienting (Hyper)graphs Under Explorable Stochastic Uncertainty. In 29th Annual European Symposium on Algorithms (ESA 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 204, pp. 10:1-10:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{bampis_et_al:LIPIcs.ESA.2021.10,
  author =	{Bampis, Evripidis and D\"{u}rr, Christoph and Erlebach, Thomas and de Lima, Murilo Santos and Megow, Nicole and Schl\"{o}ter, Jens},
  title =	{{Orienting (Hyper)graphs Under Explorable Stochastic Uncertainty}},
  booktitle =	{29th Annual European Symposium on Algorithms (ESA 2021)},
  pages =	{10:1--10:18},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-204-4},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{204},
  editor =	{Mutzel, Petra and Pagh, Rasmus and Herman, Grzegorz},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.ESA.2021.10},
  URN =		{urn:nbn:de:0030-drops-145910},
  doi =		{10.4230/LIPIcs.ESA.2021.10},
  annote =	{Keywords: Explorable uncertainty, queries, stochastic optimization, graph orientation, selection problems}
}
Document
DOI: 10.4230/DagRep.10.2.50
Scheduling (Dagstuhl Seminar 20081)

Authors: Nicole Megow, David Shmoys, and Ola Svensson

Published in: Dagstuhl Reports, Volume 10, Issue 2 (2020)

Abstract
This report documents the program and the outcomes of Dagstuhl Seminar 20081 "Scheduling". The seminar focused on the interplay between scheduling problems and problems that arise in the management of transportation and traffic. Important aspects at the intersection of these two research directions include data-driven approaches in dynamic decision-making, scheduling in combination with routing, shared mobility, and coordination versus competition.
Cite as

Nicole Megow, David Shmoys, and Ola Svensson. Scheduling (Dagstuhl Seminar 20081). In Dagstuhl Reports, Volume 10, Issue 2, pp. 50-75, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

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@Article{megow_et_al:DagRep.10.2.50,
  author =	{Megow, Nicole and Shmoys, David and Svensson, Ola},
  title =	{{Scheduling (Dagstuhl Seminar 20081)}},
  pages =	{50--75},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2020},
  volume =	{10},
  number =	{2},
  editor =	{Megow, Nicole and Shmoys, David and Svensson, Ola},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagRep.10.2.50},
  URN =		{urn:nbn:de:0030-drops-130590},
  doi =		{10.4230/DagRep.10.2.50},
  annote =	{Keywords: scheduling, optimization, approximation algorithms, routing, transportation, mechanism design}
}
Document
DOI: 10.4230/LIPIcs.ESA.2020.41
Optimally Handling Commitment Issues in Online Throughput Maximization

Authors: Franziska Eberle, Nicole Megow, and Kevin Schewior

Published in: LIPIcs, Volume 173, 28th Annual European Symposium on Algorithms (ESA 2020)

Abstract
We consider a fundamental online scheduling problem in which jobs with processing times and deadlines arrive online over time at their release dates. The task is to determine a feasible preemptive schedule on m machines that maximizes the number of jobs that complete before their deadline. Due to strong impossibility results for competitive analysis, it is commonly required that jobs contain some slack ε > 0, which means that the feasible time window for scheduling a job is at least 1+ε times its processing time. In this paper, we answer the question on how to handle commitment requirements which enforce that a scheduler has to guarantee at a certain point in time the completion of admitted jobs. This is very relevant, e.g., in providing cloud-computing services and disallows last-minute rejections of critical tasks. We present the first online algorithm for handling commitment on parallel machines for arbitrary slack ε. When the scheduler must commit upon starting a job, the algorithm is Θ(1/ε)-competitive. Somewhat surprisingly, this is the same optimal performance bound (up to constants) as for scheduling without commitment on a single machine. If commitment decisions must be made before a job’s slack becomes less than a δ-fraction of its size, we prove a competitive ratio of 𝒪(1/(ε - δ)) for 0 < δ < ε. This result nicely interpolates between commitment upon starting a job and commitment upon arrival. For the latter commitment model, it is known that no (randomized) online algorithms admits any bounded competitive ratio.
Cite as

Franziska Eberle, Nicole Megow, and Kevin Schewior. Optimally Handling Commitment Issues in Online Throughput Maximization. In 28th Annual European Symposium on Algorithms (ESA 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 173, pp. 41:1-41:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

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@InProceedings{eberle_et_al:LIPIcs.ESA.2020.41,
  author =	{Eberle, Franziska and Megow, Nicole and Schewior, Kevin},
  title =	{{Optimally Handling Commitment Issues in Online Throughput Maximization}},
  booktitle =	{28th Annual European Symposium on Algorithms (ESA 2020)},
  pages =	{41:1--41:15},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-162-7},
  ISSN =	{1868-8969},
  year =	{2020},
  volume =	{173},
  editor =	{Grandoni, Fabrizio and Herman, Grzegorz and Sanders, Peter},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.ESA.2020.41},
  URN =		{urn:nbn:de:0030-drops-129076},
  doi =		{10.4230/LIPIcs.ESA.2020.41},
  annote =	{Keywords: Deadline scheduling, throughput, online algorithms, competitive analysis}
}
Document
DOI: 10.4230/LIPIcs.MFCS.2020.18
Computing a Minimum-Cost k-Hop Steiner Tree in Tree-Like Metrics

Authors: Martin Böhm, Ruben Hoeksma, Nicole Megow, Lukas Nölke, and Bertrand Simon

Published in: LIPIcs, Volume 170, 45th International Symposium on Mathematical Foundations of Computer Science (MFCS 2020)

Abstract
We consider the problem of computing a Steiner tree of minimum cost under a k-hop constraint which requires the depth of the tree to be at most k. Our main result is an exact algorithm for metrics induced by graphs of bounded treewidth that runs in time n^O(k). For the special case of a path, we give a simple algorithm that solves the problem in polynomial time, even if k is part of the input. The main result can be used to obtain, in quasi-polynomial time, a near-optimal solution that violates the k-hop constraint by at most one hop for more general metrics induced by graphs of bounded highway dimension and bounded doubling dimension.
Cite as

Martin Böhm, Ruben Hoeksma, Nicole Megow, Lukas Nölke, and Bertrand Simon. Computing a Minimum-Cost k-Hop Steiner Tree in Tree-Like Metrics. In 45th International Symposium on Mathematical Foundations of Computer Science (MFCS 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 170, pp. 18:1-18:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

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@InProceedings{bohm_et_al:LIPIcs.MFCS.2020.18,
  author =	{B\"{o}hm, Martin and Hoeksma, Ruben and Megow, Nicole and N\"{o}lke, Lukas and Simon, Bertrand},
  title =	{{Computing a Minimum-Cost k-Hop Steiner Tree in Tree-Like Metrics}},
  booktitle =	{45th International Symposium on Mathematical Foundations of Computer Science (MFCS 2020)},
  pages =	{18:1--18:15},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-159-7},
  ISSN =	{1868-8969},
  year =	{2020},
  volume =	{170},
  editor =	{Esparza, Javier and Kr\'{a}l', Daniel},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.MFCS.2020.18},
  URN =		{urn:nbn:de:0030-drops-126870},
  doi =		{10.4230/LIPIcs.MFCS.2020.18},
  annote =	{Keywords: k-hop Steiner tree, dynamic programming, bounded treewidth}
}
Document
APPROX
DOI: 10.4230/LIPIcs.APPROX/RANDOM.2020.37
Online Minimum Cost Matching with Recourse on the Line

Authors: Nicole Megow and Lukas Nölke

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

Abstract
In online minimum cost matching on the line, n requests appear one by one and have to be matched immediately and irrevocably to a given set of servers, all on the real line. The goal is to minimize the sum of distances from the requests to their respective servers. Despite all research efforts, it remains an intriguing open question whether there exists an O(1)-competitive algorithm. The best known online algorithm by Raghvendra [S. Raghvendra, 2018] achieves a competitive factor of Θ(log n). This result matches a lower bound of Ω(log n) [A. Antoniadis et al., 2018] that holds for a quite large class of online algorithms, including all deterministic algorithms in the literature. In this work, we approach the problem in a recourse model where we allow to revoke online decisions to some extent, i.e., we allow to reassign previously matched edges. We show an O(1)-competitive algorithm for online matching on the line with amortized recourse of O(log n). This is the first non-trivial result for min-cost bipartite matching with recourse. For so-called alternating instances, with no more than one request between two servers, we obtain a near-optimal result. We give a (1+ε)-competitive algorithm that reassigns any request at most O(ε^{-1.001}) times. This special case is interesting as the aforementioned quite general lower bound Ω(log n) holds for such instances.
Cite as

Nicole Megow and Lukas Nölke. Online Minimum Cost Matching with Recourse on the Line. In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 176, pp. 37:1-37:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

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@InProceedings{megow_et_al:LIPIcs.APPROX/RANDOM.2020.37,
  author =	{Megow, Nicole and N\"{o}lke, Lukas},
  title =	{{Online Minimum Cost Matching with Recourse on the Line}},
  booktitle =	{Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2020)},
  pages =	{37:1--37:16},
  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-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.APPROX/RANDOM.2020.37},
  URN =		{urn:nbn:de:0030-drops-126401},
  doi =		{10.4230/LIPIcs.APPROX/RANDOM.2020.37},
  annote =	{Keywords: min-cost matching in bipartite graphs, recourse, competitive analysis, online}
}
Document
DOI: 10.4230/OASIcs.NG-RES.2020.2
Energy Minimization in DAG Scheduling on MPSoCs at Run-Time: Theory and Practice

Authors: Bertrand Simon, Joachim Falk, Nicole Megow, and Jürgen Teich

Published in: OASIcs, Volume 77, Workshop on Next Generation Real-Time Embedded Systems (NG-RES 2020)

Abstract
Static (offline) techniques for mapping applications given by task graphs to MPSoC systems often deliver overly pessimistic and thus suboptimal results w.r.t. exploiting time slack in order to minimize the energy consumption. This holds true in particular in case computation times of tasks may be workload-dependent and becoming known only at runtime or in case of conditionally executed tasks or scenarios. This paper studies and quantitatively evaluates different classes of algorithms for scheduling periodic applications given by task graphs (i.e., DAGs) with precedence constraints and a global deadline on homogeneous MPSoCs purely at runtime on a per-instance base. We present and analyze algorithms providing provably optimal results as well as approximation algorithms with proven guarantees on the achieved energy savings. For problem instances taken from realistic embedded system benchmarks as well as synthetic scalable problems, we provide results on the computation time and quality of each algorithm to perform a) scheduling and b) voltage/speed assignments for each task at runtime. In our portfolio, we distinguish as well continuous and discrete speed (e.g., DVFS-related) assignment problems. In summary, the presented ties between theory (algorithmic complexity and optimality) and execution time analysis deliver important insights on the practical usability of the presented algorithms for runtime optimization of task scheduling and speed assignment on MPSoCs.
Cite as

Bertrand Simon, Joachim Falk, Nicole Megow, and Jürgen Teich. Energy Minimization in DAG Scheduling on MPSoCs at Run-Time: Theory and Practice. In Workshop on Next Generation Real-Time Embedded Systems (NG-RES 2020). Open Access Series in Informatics (OASIcs), Volume 77, pp. 2:1-2:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

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@InProceedings{simon_et_al:OASIcs.NG-RES.2020.2,
  author =	{Simon, Bertrand and Falk, Joachim and Megow, Nicole and Teich, J\"{u}rgen},
  title =	{{Energy Minimization in DAG Scheduling on MPSoCs at Run-Time: Theory and Practice}},
  booktitle =	{Workshop on Next Generation Real-Time Embedded Systems (NG-RES 2020)},
  pages =	{2:1--2:13},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-136-8},
  ISSN =	{2190-6807},
  year =	{2020},
  volume =	{77},
  editor =	{Bertogna, Marko and Terraneo, Federico},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/OASIcs.NG-RES.2020.2},
  URN =		{urn:nbn:de:0030-drops-117781},
  doi =		{10.4230/OASIcs.NG-RES.2020.2},
  annote =	{Keywords: energy minimization, speed scaling, precedence graphs, scheduling, critical path, MPSoC}
}
Document
Artifact
DOI: 10.4230/DARTS.5.1.6
Scheduling Self-Suspending Tasks: New and Old Results (Artifact)

Authors: Jian-Jia Chen, Tobias Hahn, Ruben Hoeksma, Nicole Megow, and Georg von der Brüggen

Published in: DARTS, Volume 5, Issue 1, Special Issue of the 31st Euromicro Conference on Real-Time Systems (ECRTS 2019)

Abstract
In computing systems, a job may suspend itself (before it finishes its execution) when it has to wait for certain results from other (usually external) activities. For real-time systems, such self-suspension behavior has been shown to induce performance degradation. Hence, the researchers in the real-time systems community have devoted themselves to the design and analysis of scheduling algorithms that can alleviate the performance penalty due to self-suspension behavior. As self-suspension and delegation of parts of a job to non-bottleneck resources is pretty natural in many applications, researchers in the operations research (OR) community have also explored scheduling algorithms for systems with such suspension behavior, called the master-slave problem in the OR community. This paper first reviews the results for the master-slave problem in the OR literature and explains their impact on several long-standing problems for scheduling self-suspending real-time tasks. For frame-based periodic real-time tasks, in which the periods of all tasks are identical and all jobs related to one frame are released synchronously, we explore different approximation metrics with respect to resource augmentation factors under different scenarios for both uniprocessor and multiprocessor systems, and demonstrate that different approximation metrics can create different levels of difficulty for the approximation. Our experimental results show that such more carefully designed schedules can significantly outperform the state-of-the-art.
Cite as

Jian-Jia Chen, Tobias Hahn, Ruben Hoeksma, Nicole Megow, and Georg von der Brüggen. Scheduling Self-Suspending Tasks: New and Old Results (Artifact). In Special Issue of the 31st Euromicro Conference on Real-Time Systems (ECRTS 2019). Dagstuhl Artifacts Series (DARTS), Volume 5, Issue 1, pp. 6:1-6:3, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)

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@Article{chen_et_al:DARTS.5.1.6,
  author =	{Chen, Jian-Jia and Hahn, Tobias and Hoeksma, Ruben and Megow, Nicole and von der Br\"{u}ggen, Georg},
  title =	{{Scheduling Self-Suspending Tasks: New and Old Results}},
  pages =	{6:1--6:3},
  journal =	{Dagstuhl Artifacts Series},
  ISSN =	{2509-8195},
  year =	{2019},
  volume =	{5},
  number =	{1},
  editor =	{Chen, Jian-Jia and Hahn, Tobias and Hoeksma, Ruben and Megow, Nicole and von der Br\"{u}ggen, Georg},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DARTS.5.1.6},
  URN =		{urn:nbn:de:0030-drops-107349},
  doi =		{10.4230/DARTS.5.1.6},
  annote =	{Keywords: Self-suspension, master-slave problem, computational complexity, speedup factors}
}
Document
DOI: 10.4230/LIPIcs.ECRTS.2019.16
Scheduling Self-Suspending Tasks: New and Old Results

Authors: Jian-Jia Chen, Tobias Hahn, Ruben Hoeksma, Nicole Megow, and Georg von der Brüggen

Published in: LIPIcs, Volume 133, 31st Euromicro Conference on Real-Time Systems (ECRTS 2019)

Abstract
In computing systems, a job may suspend itself (before it finishes its execution) when it has to wait for certain results from other (usually external) activities. For real-time systems, such self-suspension behavior has been shown to induce performance degradation. Hence, the researchers in the real-time systems community have devoted themselves to the design and analysis of scheduling algorithms that can alleviate the performance penalty due to self-suspension behavior. As self-suspension and delegation of parts of a job to non-bottleneck resources is pretty natural in many applications, researchers in the operations research (OR) community have also explored scheduling algorithms for systems with such suspension behavior, called the master-slave problem in the OR community. This paper first reviews the results for the master-slave problem in the OR literature and explains their impact on several long-standing problems for scheduling self-suspending real-time tasks. For frame-based periodic real-time tasks, in which the periods of all tasks are identical and all jobs related to one frame are released synchronously, we explore different approximation metrics with respect to resource augmentation factors under different scenarios for both uniprocessor and multiprocessor systems, and demonstrate that different approximation metrics can create different levels of difficulty for the approximation. Our experimental results show that such more carefully designed schedules can significantly outperform the state-of-the-art.
Cite as

Jian-Jia Chen, Tobias Hahn, Ruben Hoeksma, Nicole Megow, and Georg von der Brüggen. Scheduling Self-Suspending Tasks: New and Old Results. In 31st Euromicro Conference on Real-Time Systems (ECRTS 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 133, pp. 16:1-16:23, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)

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@InProceedings{chen_et_al:LIPIcs.ECRTS.2019.16,
  author =	{Chen, Jian-Jia and Hahn, Tobias and Hoeksma, Ruben and Megow, Nicole and von der Br\"{u}ggen, Georg},
  title =	{{Scheduling Self-Suspending Tasks: New and Old Results}},
  booktitle =	{31st Euromicro Conference on Real-Time Systems (ECRTS 2019)},
  pages =	{16:1--16:23},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-110-8},
  ISSN =	{1868-8969},
  year =	{2019},
  volume =	{133},
  editor =	{Quinton, Sophie},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.ECRTS.2019.16},
  URN =		{urn:nbn:de:0030-drops-107532},
  doi =		{10.4230/LIPIcs.ECRTS.2019.16},
  annote =	{Keywords: Self-suspension, master-slave problem, computational complexity, speedup factors}
}
Document
DOI: 10.4230/DagRep.8.3.1
Scheduling (Dagstuhl Seminar 18101)

Authors: Magnús M. Halldórson, Nicole Megow, and Clifford Stein

Published in: Dagstuhl Reports, Volume 8, Issue 3 (2018)

Abstract
This report documents the program and outcomes of the Dagstuhl Seminar 18101 "Scheduling" in March 2018. The seminar brought together algorithmically oriented researchers from two communities with interests in resource management: (i) the scheduling community and (ii) the networking and distributed computing community. The primary objective of the seminar was to expose each community to the important problems and techniques from the other community, and to facilitate dialog and collaboration between researchers.
Cite as

Magnús M. Halldórson, Nicole Megow, and Clifford Stein. Scheduling (Dagstuhl Seminar 18101). In Dagstuhl Reports, Volume 8, Issue 3, pp. 1-20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)

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@Article{halldorson_et_al:DagRep.8.3.1,
  author =	{Halld\'{o}rson, Magn\'{u}s M. and Megow, Nicole and Stein, Clifford},
  title =	{{Scheduling (Dagstuhl Seminar 18101)}},
  pages =	{1--20},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2018},
  volume =	{8},
  number =	{3},
  editor =	{Halld\'{o}rson, Magn\'{u}s M. and Megow, Nicole and Stein, Clifford},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagRep.8.3.1},
  URN =		{urn:nbn:de:0030-drops-92942},
  doi =		{10.4230/DagRep.8.3.1},
  annote =	{Keywords: scheduling, optimization, approximation algorithms}
}
Document
DOI: 10.4230/LIPIcs.ITCS.2018.30
Scheduling with Explorable Uncertainty

Authors: Christoph Dürr, Thomas Erlebach, Nicole Megow, and Julie Meißner

Published in: LIPIcs, Volume 94, 9th Innovations in Theoretical Computer Science Conference (ITCS 2018)

Abstract
We introduce a novel model for scheduling with explorable uncertainty. In this model, the processing time of a job can potentially be reduced (by an a priori unknown amount) by testing the job. Testing a job j takes one unit of time and may reduce its processing time from the given upper limit p'_j (which is the time taken to execute the job if it is not tested) to any value between 0 and p'_j. This setting is motivated e.g. by applications where a code optimizer can be run on a job before executing it. We consider the objective of minimizing the sum of completion times on a single machine. All jobs are available from the start, but the reduction in their processing times as a result of testing is unknown, making this an online problem that is amenable to competitive analysis. The need to balance the time spent on tests and the time spent on job executions adds a novel flavor to the problem. We give the first and nearly tight lower and upper bounds on the competitive ratio for deterministic and randomized algorithms. We also show that minimizing the makespan is a considerably easier problem for which we give optimal deterministic and randomized online algorithms.
Cite as

Christoph Dürr, Thomas Erlebach, Nicole Megow, and Julie Meißner. Scheduling with Explorable Uncertainty. In 9th Innovations in Theoretical Computer Science Conference (ITCS 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 94, pp. 30:1-30:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)

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@InProceedings{durr_et_al:LIPIcs.ITCS.2018.30,
  author =	{D\"{u}rr, Christoph and Erlebach, Thomas and Megow, Nicole and Mei{\ss}ner, Julie},
  title =	{{Scheduling with Explorable Uncertainty}},
  booktitle =	{9th Innovations in Theoretical Computer Science Conference (ITCS 2018)},
  pages =	{30:1--30:14},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-060-6},
  ISSN =	{1868-8969},
  year =	{2018},
  volume =	{94},
  editor =	{Karlin, Anna R.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.ITCS.2018.30},
  URN =		{urn:nbn:de:0030-drops-83360},
  doi =		{10.4230/LIPIcs.ITCS.2018.30},
  annote =	{Keywords: online scheduling, explorable uncertainty, competitive ratio, makespan, sum of completion times}
}
Document
DOI: 10.4230/LIPIcs.SEA.2017.22
Minimum Spanning Tree under Explorable Uncertainty in Theory and Experiments

Authors: Jacob Focke, Nicole Megow, and Julie Meißner

Published in: LIPIcs, Volume 75, 16th International Symposium on Experimental Algorithms (SEA 2017)

Abstract
We consider the minimum spanning tree (MST) problem in an uncertainty model where uncertain edge weights can be explored at extra cost. The task is to find an MST by querying a minimum number of edges for their exact weight. This problem has received quite some attention from the algorithms theory community. In this paper, we conduct the first practical experiments for MST under uncertainty, theoretically compare three known algorithms, and compare theoretical with practical behavior of the algorithms. Among others, we observe that the average performance and the absolute number of queries are both far from the theoretical worst-case bounds. Furthermore, we investigate a known general preprocessing procedure and develop an implementation thereof that maximally reduces the data uncertainty. We also characterize a class of instances that is solved completely by our preprocessing. Our experiments are based on practical data from an application in telecommunications and uncertainty instances generated from the standard TSPLib graph library.
Cite as

Jacob Focke, Nicole Megow, and Julie Meißner. Minimum Spanning Tree under Explorable Uncertainty in Theory and Experiments. In 16th International Symposium on Experimental Algorithms (SEA 2017). Leibniz International Proceedings in Informatics (LIPIcs), Volume 75, pp. 22:1-22:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2017)

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@InProceedings{focke_et_al:LIPIcs.SEA.2017.22,
  author =	{Focke, Jacob and Megow, Nicole and Mei{\ss}ner, Julie},
  title =	{{Minimum Spanning Tree under Explorable Uncertainty in Theory and Experiments}},
  booktitle =	{16th International Symposium on Experimental Algorithms (SEA 2017)},
  pages =	{22:1--22:14},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-036-1},
  ISSN =	{1868-8969},
  year =	{2017},
  volume =	{75},
  editor =	{Iliopoulos, Costas S. and Pissis, Solon P. and Puglisi, Simon J. and Raman, Rajeev},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.SEA.2017.22},
  URN =		{urn:nbn:de:0030-drops-76202},
  doi =		{10.4230/LIPIcs.SEA.2017.22},
  annote =	{Keywords: MST, explorable uncertainty, competitive ratio, experimental algorithms}
}
Document
DOI: 10.4230/DagRep.6.2.97
Scheduling (Dagstuhl Seminar 16081)

Authors: Nikhil Bansal, Nicole Megow, and Clifford Stein

Published in: Dagstuhl Reports, Volume 6, Issue 2 (2016)

Abstract
This report documents the program and the outcomes of Dagstuhl Seminar 16081 "Scheduling". The seminar was centered around recent new developments, discussion of open problems and exploring future research directions within the broader scheduling community.
Cite as

Nikhil Bansal, Nicole Megow, and Clifford Stein. Scheduling (Dagstuhl Seminar 16081). In Dagstuhl Reports, Volume 6, Issue 2, pp. 97-118, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2016)

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@Article{bansal_et_al:DagRep.6.2.97,
  author =	{Bansal, Nikhil and Megow, Nicole and Stein, Clifford},
  title =	{{Scheduling (Dagstuhl Seminar 16081)}},
  pages =	{97--118},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2016},
  volume =	{6},
  number =	{2},
  editor =	{Bansal, Nikhil and Megow, Nicole and Stein, Clifford},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagRep.6.2.97},
  URN =		{urn:nbn:de:0030-drops-58902},
  doi =		{10.4230/DagRep.6.2.97},
  annote =	{Keywords: approximation algorithms, scheduling, optimization}
}
Document
DOI: 10.4230/LIPIcs.APPROX-RANDOM.2015.175
Stochastic and Robust Scheduling in the Cloud

Authors: Lin Chen, Nicole Megow, Roman Rischke, and Leen Stougie

Published in: LIPIcs, Volume 40, Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2015)

Abstract
Users of cloud computing services are offered rapid access to computing resources via the Internet. Cloud providers use different pricing options such as (i) time slot reservation in advance at a fixed price and (ii) on-demand service at a (hourly) pay-as-used basis. Choosing the best combination of pricing options is a challenging task for users, in particular, when the instantiation of computing jobs underlies uncertainty. We propose a natural model for two-stage scheduling under uncertainty that captures such resource provisioning and scheduling problem in the cloud. Reserving a time unit for processing jobs incurs some cost, which depends on when the reservation is made: a priori decisions, based only on distributional information, are much cheaper than on-demand decisions when the actual scenario is known. We consider both stochastic and robust versions of scheduling unrelated machines with objectives of minimizing the sum of weighted completion times and the makespan. Our main contribution is an (8+eps)-approximation algorithm for the min-sum objective for the stochastic polynomial-scenario model. The same technique gives a (7.11+eps)-approximation for minimizing the makespan. The key ingredient is an LP-based separation of jobs and time slots to be considered in either the first or the second stage only, and then approximately solving the separated problems. At the expense of another epsilon our results hold for any arbitrary scenario distribution given by means of a black-box. Our techniques also yield approximation algorithms for robust two-stage scheduling.
Cite as

Lin Chen, Nicole Megow, Roman Rischke, and Leen Stougie. Stochastic and Robust Scheduling in the Cloud. In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2015). Leibniz International Proceedings in Informatics (LIPIcs), Volume 40, pp. 175-186, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2015)

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@InProceedings{chen_et_al:LIPIcs.APPROX-RANDOM.2015.175,
  author =	{Chen, Lin and Megow, Nicole and Rischke, Roman and Stougie, Leen},
  title =	{{Stochastic and Robust Scheduling in the Cloud}},
  booktitle =	{Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2015)},
  pages =	{175--186},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-939897-89-7},
  ISSN =	{1868-8969},
  year =	{2015},
  volume =	{40},
  editor =	{Garg, Naveen and Jansen, Klaus and Rao, Anup and Rolim, Jos\'{e} D. P.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.APPROX-RANDOM.2015.175},
  URN =		{urn:nbn:de:0030-drops-53028},
  doi =		{10.4230/LIPIcs.APPROX-RANDOM.2015.175},
  annote =	{Keywords: Approximation Algorithms, Robust Optimization, Stochastic Optimization, Unrelated Machine Scheduling, Cloud Computing}
}
Document
DOI: 10.4230/LIPIcs.STACS.2014.276
Packing a Knapsack of Unknown Capacity

Authors: Yann Disser, Max Klimm, Nicole Megow, and Sebastian Stiller

Published in: LIPIcs, Volume 25, 31st International Symposium on Theoretical Aspects of Computer Science (STACS 2014)

Abstract
We study the problem of packing a knapsack without knowing its capacity. Whenever we attempt to pack an item that does not fit, the item is discarded; if the item fits, we have to include it in the packing. We show that there is always a policy that packs a value within factor 2 of the optimum packing, irrespective of the actual capacity. If all items have unit density, we achieve a factor equal to the golden ratio. Both factors are shown to be best possible. In fact, we obtain the above factors using packing policies that are universal in the sense that they fix a particular order of the items and try to pack the items in this order, independent of the observations made while packing. We give efficient algorithms computing these policies. On the other hand, we show that, for any a>1, the problem of deciding whether a given universal policy achieves a factor of a is coNP-complete. If a is part of the input, the same problem is shown to be coNP-complete for items with unit densities. Finally, we show that it is coNP-hard to decide, for given a, whether a set of items admits a universal policy with factor a, even if all items have unit densities.
Cite as

Yann Disser, Max Klimm, Nicole Megow, and Sebastian Stiller. Packing a Knapsack of Unknown Capacity. In 31st International Symposium on Theoretical Aspects of Computer Science (STACS 2014). Leibniz International Proceedings in Informatics (LIPIcs), Volume 25, pp. 276-287, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2014)

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@InProceedings{disser_et_al:LIPIcs.STACS.2014.276,
  author =	{Disser, Yann and Klimm, Max and Megow, Nicole and Stiller, Sebastian},
  title =	{{Packing a Knapsack of Unknown Capacity}},
  booktitle =	{31st International Symposium on Theoretical Aspects of Computer Science (STACS 2014)},
  pages =	{276--287},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-939897-65-1},
  ISSN =	{1868-8969},
  year =	{2014},
  volume =	{25},
  editor =	{Mayr, Ernst W. and Portier, Natacha},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.STACS.2014.276},
  URN =		{urn:nbn:de:0030-drops-44642},
  doi =		{10.4230/LIPIcs.STACS.2014.276},
  annote =	{Keywords: Knapsack, unknown capacity, robustness, approximation algorithms}
}
Document
DOI: 10.4230/DagSemProc.10071.3
10071 Open Problems – Scheduling

Authors: Jim Anderson, Björn Andersson, Yossi Azar, Nikhil Bansal, Enrico Bini, Marek Chrobak, José Correa, Liliana Cucu-Grosjean, Rob Davis, Arvind Easwaran, Jeff Edmonds, Shelby Funk, Sathish Gopalakrishnan, Han Hoogeveen, Claire Mathieu, Nicole Megow, Seffi Naor, Kirk Pruhs, Maurice Queyranne, Adi Rosén, Nicolas Schabanel, Jiří Sgall, René Sitters, Sebastian Stiller, Marc Uetz, Tjark Vredeveld, and Gerhard J. Woeginger

Published in: Dagstuhl Seminar Proceedings, Volume 10071, Scheduling (2010)

Abstract
Collection of the open problems presented at the scheduling seminar.
Cite as

Jim Anderson, Björn Andersson, Yossi Azar, Nikhil Bansal, Enrico Bini, Marek Chrobak, José Correa, Liliana Cucu-Grosjean, Rob Davis, Arvind Easwaran, Jeff Edmonds, Shelby Funk, Sathish Gopalakrishnan, Han Hoogeveen, Claire Mathieu, Nicole Megow, Seffi Naor, Kirk Pruhs, Maurice Queyranne, Adi Rosén, Nicolas Schabanel, Jiří Sgall, René Sitters, Sebastian Stiller, Marc Uetz, Tjark Vredeveld, and Gerhard J. Woeginger. 10071 Open Problems – Scheduling. In Scheduling. Dagstuhl Seminar Proceedings, Volume 10071, pp. 1-24, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2010)

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@InProceedings{anderson_et_al:DagSemProc.10071.3,
  author =	{Anderson, Jim and Andersson, Bj\"{o}rn and Azar, Yossi and Bansal, Nikhil and Bini, Enrico and Chrobak, Marek and Correa, Jos\'{e} and Cucu-Grosjean, Liliana and Davis, Rob and Easwaran, Arvind and Edmonds, Jeff and Funk, Shelby and Gopalakrishnan, Sathish and Hoogeveen, Han and Mathieu, Claire and Megow, Nicole and Naor, Seffi and Pruhs, Kirk and Queyranne, Maurice and Ros\'{e}n, Adi and Schabanel, Nicolas and Sgall, Ji\v{r}{\'\i} and Sitters, Ren\'{e} and Stiller, Sebastian and Uetz, Marc and Vredeveld, Tjark and Woeginger, Gerhard J.},
  title =	{{10071 Open Problems – Scheduling}},
  booktitle =	{Scheduling},
  pages =	{1--24},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2010},
  volume =	{10071},
  editor =	{Susanne Albers and Sanjoy K. Baruah and Rolf H. M\"{o}hring and Kirk Pruhs},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagSemProc.10071.3},
  URN =		{urn:nbn:de:0030-drops-25367},
  doi =		{10.4230/DagSemProc.10071.3},
  annote =	{Keywords: Open problems, scheduling}
}
Document
DOI: 10.4230/DagSemProc.05031.15
Models and Algorithms for Stochastic Online Scheduling

Authors: Nicole Megow, Marc Uetz, and Tjark Vredeveld

Published in: Dagstuhl Seminar Proceedings, Volume 5031, Algorithms for Optimization with Incomplete Information (2005)

Abstract
We introduce a model for non-preemptive scheduling under uncertainty. In this model, we combine the main characteristics of online and stochastic scheduling in a simple and natural way. Job processing times are assumed to be stochastic, but in contrast to the classical stochastic scheduling models, we assume that jobs arrive online over time, and there is no knowledge about the jobs that will arrive in the future. The model incorporates both, stochastic scheduling and online scheduling as a special case. The particular setting we analyze is parallel machine scheduling, with the objective to minimize the total weighted completion times of jobs. We propose simple, combinatorial online scheduling policies for that model, and derive performance guarantees that match the currently best known performance guarantees for stochastic parallel machine scheduling. For processing times that follow NBUE distributions, we improve upon previously best known performance bounds, even though we consider a more general setting.
Cite as

Nicole Megow, Marc Uetz, and Tjark Vredeveld. Models and Algorithms for Stochastic Online Scheduling. In Algorithms for Optimization with Incomplete Information. Dagstuhl Seminar Proceedings, Volume 5031, pp. 1-4, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2005)

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@InProceedings{megow_et_al:DagSemProc.05031.15,
  author =	{Megow, Nicole and Uetz, Marc and Vredeveld, Tjark},
  title =	{{Models and Algorithms for Stochastic Online Scheduling}},
  booktitle =	{Algorithms for Optimization with Incomplete Information},
  pages =	{1--4},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2005},
  volume =	{5031},
  editor =	{Susanne Albers and Rolf H. M\"{o}hring and Georg Ch. Pflug and R\"{u}diger Schultz},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagSemProc.05031.15},
  URN =		{urn:nbn:de:0030-drops-1106},
  doi =		{10.4230/DagSemProc.05031.15},
  annote =	{Keywords: stochastic scheduling , online optimization , weighted completion time}
}
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