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Documents authored by Figiel, Aleksander

Document
DOI: 10.4230/LIPIcs.OPODIS.2024.38
Efficient Algorithms for Demand-Aware Networks and a Connection to Virtual Network Embedding

Authors: Aleksander Figiel, Janne H. Korhonen, Neil Olver, and Stefan Schmid

Published in: LIPIcs, Volume 324, 28th International Conference on Principles of Distributed Systems (OPODIS 2024)

Abstract
Emerging optical switching technologies enable demand-aware datacenter networks, whose topology can be flexibly optimized toward the traffic they serve. This paper revisits the bounded-degree network design problem underlying such demand-aware networks. Namely, given a distribution over communicating node pairs (represented has a demand graph), we want to design a network with bounded maximum degree (called host graph) that minimizes the expected communication distance. We improve the understanding of this problem domain by filling several gaps in prior work. First, we present the first practical algorithm for solving this problem on arbitrary instances without violating the degree bound. Our algorithm is based on novel insights obtained from studying a new Steiner node version of the problem, and we report on an extensive empirical evaluation, using several real-world traffic traces from datacenters, finding that our approach results in improved demand-aware network designs. Second, we shed light on the complexity and hardness of the bounded-degree network design problem by formally establishing its NP-completeness for any degree. We use our techniques to improve prior upper bounds for sparse instances. Finally, we study an intriguing connection between demand-aware network design and the virtual networking embedding problem, and show that the latter cannot be used to approximate the former: there is no universal host graph which can provide a constant approximation for our problem.
Cite as

Aleksander Figiel, Janne H. Korhonen, Neil Olver, and Stefan Schmid. Efficient Algorithms for Demand-Aware Networks and a Connection to Virtual Network Embedding. In 28th International Conference on Principles of Distributed Systems (OPODIS 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 324, pp. 38:1-38:24, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{figiel_et_al:LIPIcs.OPODIS.2024.38,
  author =	{Figiel, Aleksander and Korhonen, Janne H. and Olver, Neil and Schmid, Stefan},
  title =	{{Efficient Algorithms for Demand-Aware Networks and a Connection to Virtual Network Embedding}},
  booktitle =	{28th International Conference on Principles of Distributed Systems (OPODIS 2024)},
  pages =	{38:1--38:24},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-360-7},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{324},
  editor =	{Bonomi, Silvia and Galletta, Letterio and Rivi\`{e}re, Etienne and Schiavoni, Valerio},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.OPODIS.2024.38},
  URN =		{urn:nbn:de:0030-drops-225742},
  doi =		{10.4230/LIPIcs.OPODIS.2024.38},
  annote =	{Keywords: demand-aware networks, algorithms, virtual network embedding}
}
Document
DOI: 10.4230/LIPIcs.ESA.2023.47
Correlating Theory and Practice in Finding Clubs and Plexes

Authors: Aleksander Figiel, Tomohiro Koana, André Nichterlein, and Niklas Wünsche

Published in: LIPIcs, Volume 274, 31st Annual European Symposium on Algorithms (ESA 2023)

Abstract
For solving NP-hard problems there is often a huge gap between theoretical guarantees and observed running times on real-world instances. As a first step towards tackling this issue, we propose an approach to quantify the correlation between theoretical and observed running times. We use two NP-hard problems related to finding large "cliquish" subgraphs in a given graph as demonstration of this measure. More precisely, we focus on finding maximum s-clubs and s-plexes, i. e., graphs of diameter s and graphs where each vertex is adjacent to all but s vertices. Preprocessing based on Turing kernelization is a standard tool to tackle these problems, especially on sparse graphs. We provide a parameterized analysis for the Turing kernelization and demonstrate their usefulness in practice. Moreover, we demonstrate that our measure indeed captures the correlation between these new theoretical and the observed running times.
Cite as

Aleksander Figiel, Tomohiro Koana, André Nichterlein, and Niklas Wünsche. Correlating Theory and Practice in Finding Clubs and Plexes. In 31st Annual European Symposium on Algorithms (ESA 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 274, pp. 47:1-47:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{figiel_et_al:LIPIcs.ESA.2023.47,
  author =	{Figiel, Aleksander and Koana, Tomohiro and Nichterlein, Andr\'{e} and W\"{u}nsche, Niklas},
  title =	{{Correlating Theory and Practice in Finding Clubs and Plexes}},
  booktitle =	{31st Annual European Symposium on Algorithms (ESA 2023)},
  pages =	{47:1--47:18},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-295-2},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{274},
  editor =	{G{\o}rtz, Inge Li and Farach-Colton, Martin and Puglisi, Simon J. and Herman, Grzegorz},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ESA.2023.47},
  URN =		{urn:nbn:de:0030-drops-187000},
  doi =		{10.4230/LIPIcs.ESA.2023.47},
  annote =	{Keywords: Preprocessing, Turing kernelization, Pearson correlation coefficient}
}
Document
DOI: 10.4230/LIPIcs.ESA.2022.53
There and Back Again: On Applying Data Reduction Rules by Undoing Others

Authors: Aleksander Figiel, Vincent Froese, André Nichterlein, and Rolf Niedermeier

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

Abstract
Data reduction rules are an established method in the algorithmic toolbox for tackling computationally challenging problems. A data reduction rule is a polynomial-time algorithm that, given a problem instance as input, outputs an equivalent, typically smaller instance of the same problem. The application of data reduction rules during the preprocessing of problem instances allows in many cases to considerably shrink their size, or even solve them directly. Commonly, these data reduction rules are applied exhaustively and in some fixed order to obtain irreducible instances. It was often observed that by changing the order of the rules, different irreducible instances can be obtained. We propose to "undo" data reduction rules on irreducible instances, by which they become larger, and then subsequently apply data reduction rules again to shrink them. We show that this somewhat counter-intuitive approach can lead to significantly smaller irreducible instances. The process of undoing data reduction rules is not limited to "rolling back" data reduction rules applied to the instance during preprocessing. Instead, we formulate so-called backward rules, which essentially undo a data reduction rule, but without using any information about which data reduction rules were applied to it previously. In particular, based on the example of Vertex Cover we propose two methods applying backward rules to shrink the instances further. In our experiments we show that this way smaller irreducible instances consisting of real-world graphs from the SNAP and DIMACS datasets can be computed.
Cite as

Aleksander Figiel, Vincent Froese, André Nichterlein, and Rolf Niedermeier. There and Back Again: On Applying Data Reduction Rules by Undoing Others. In 30th Annual European Symposium on Algorithms (ESA 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 244, pp. 53:1-53:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{figiel_et_al:LIPIcs.ESA.2022.53,
  author =	{Figiel, Aleksander and Froese, Vincent and Nichterlein, Andr\'{e} and Niedermeier, Rolf},
  title =	{{There and Back Again: On Applying Data Reduction Rules by Undoing Others}},
  booktitle =	{30th Annual European Symposium on Algorithms (ESA 2022)},
  pages =	{53:1--53:15},
  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.dagstuhl.de/entities/document/10.4230/LIPIcs.ESA.2022.53},
  URN =		{urn:nbn:de:0030-drops-169914},
  doi =		{10.4230/LIPIcs.ESA.2022.53},
  annote =	{Keywords: Kernelization, Preprocessing, Vertex Cover}
}
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