4 Search Results for "Einziger, Gil"

Document
DOI: 10.4230/LIPIcs.OPODIS.2024.23
Distributed Recoverable Sketches

Authors: Diana Cohen, Roy Friedman, and Rana Shahout

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

Abstract
Sketches are commonly used in computer systems and network monitoring tools to provide efficient query executions while maintaining a compact data representation. Switches and routers maintain sketches to track statistical characteristics of the network traffic. The availability of such data is essential for the network analysis as a whole. Consequently, being able to recover sketches is critical following a switch crash. In this paper, we explore how nodes in a network environment can cooperate to recover sketch data whenever any of them crashes. In particular, we focus on frequency estimation linear sketches, such as the Count-Min Sketch. We consider various approaches to ensure data reliability and explore the trade-offs between space consumption, runtime overheads, and traffic during recovery, which we point out as design guidelines. Besides different aspects of efficacy, we design a modular system for ease of maintenance and further scaling. A key aspect we examine is how nodes update each other about their sketch content as it evolves over time. In particular, we compare between periodic full updates vs. incremental updates. We also examine several data structures to economically represent and encode a batch of latest changes. Our framework is generic, and other data structures can be plugged-in via an abstract API as long as they implement the corresponding API methods.
Cite as

Diana Cohen, Roy Friedman, and Rana Shahout. Distributed Recoverable Sketches. In 28th International Conference on Principles of Distributed Systems (OPODIS 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 324, pp. 23:1-23:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{cohen_et_al:LIPIcs.OPODIS.2024.23,
  author =	{Cohen, Diana and Friedman, Roy and Shahout, Rana},
  title =	{{Distributed Recoverable Sketches}},
  booktitle =	{28th International Conference on Principles of Distributed Systems (OPODIS 2024)},
  pages =	{23:1--23:16},
  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.2024.23},
  URN =		{urn:nbn:de:0030-drops-225594},
  doi =		{10.4230/LIPIcs.OPODIS.2024.23},
  annote =	{Keywords: Sketches, Stream Processing, Distributed Recovery, Incremental Updates, Sketch Partitioning}
}
Document
DOI: 10.4230/LIPIcs.MFCS.2018.34
Give Me Some Slack: Efficient Network Measurements

Authors: Ran Ben Basat, Gil Einziger, and Roy Friedman

Published in: LIPIcs, Volume 117, 43rd International Symposium on Mathematical Foundations of Computer Science (MFCS 2018)

Abstract
Many networking applications require timely access to recent network measurements, which can be captured using a sliding window model. Maintaining such measurements is a challenging task due to the fast line speed and scarcity of fast memory in routers. In this work, we study the impact of allowing slack in the window size on the asymptotic requirements of sliding window problems. That is, the algorithm can dynamically adjust the window size between W and W(1+tau) where tau is a small positive parameter. We demonstrate this model's attractiveness by showing that it enables efficient algorithms to problems such as Maximum and General-Summing that require Omega(W) bits even for constant factor approximations in the exact sliding window model. Additionally, for problems that admit sub-linear approximation algorithms such as Basic-Summing and Count-Distinct, the slack model enables a further asymptotic improvement. The main focus of the paper is on the widely studied Basic-Summing problem of computing the sum of the last W integers from {0,1 ...,R} in a stream. While it is known that Omega(W log R) bits are needed in the exact window model, we show that approximate windows allow an exponential space reduction for constant tau. Specifically, for tau=Theta(1), we present a space lower bound of Omega(log(RW)) bits. Additionally, we show an Omega(log (W/epsilon)) lower bound for RW epsilon additive approximations and a Omega(log (W/epsilon)+log log R) bits lower bound for (1+epsilon) multiplicative approximations. Our work is the first to study this problem in the exact and additive approximation settings. For all settings, we provide memory optimal algorithms that operate in worst case constant time. This strictly improves on the work of [Mayur Datar et al., 2002] for (1+epsilon)-multiplicative approximation that requires O(epsilon^(-1) log(RW)log log (RW)) space and performs updates in O(log (RW)) worst case time. Finally, we show asymptotic improvements for the Count-Distinct, General-Summing and Maximum problems.
Cite as

Ran Ben Basat, Gil Einziger, and Roy Friedman. Give Me Some Slack: Efficient Network Measurements. In 43rd International Symposium on Mathematical Foundations of Computer Science (MFCS 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 117, pp. 34:1-34:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)

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@InProceedings{benbasat_et_al:LIPIcs.MFCS.2018.34,
  author =	{Ben Basat, Ran and Einziger, Gil and Friedman, Roy},
  title =	{{Give Me Some Slack: Efficient Network Measurements}},
  booktitle =	{43rd International Symposium on Mathematical Foundations of Computer Science (MFCS 2018)},
  pages =	{34:1--34:16},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-086-6},
  ISSN =	{1868-8969},
  year =	{2018},
  volume =	{117},
  editor =	{Potapov, Igor and Spirakis, Paul and Worrell, James},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.MFCS.2018.34},
  URN =		{urn:nbn:de:0030-drops-96165},
  doi =		{10.4230/LIPIcs.MFCS.2018.34},
  annote =	{Keywords: Streaming, Network Measurements, Statistics, Lower Bounds}
}
Document
Brief Announcement
DOI: 10.4230/LIPIcs.ICALP.2018.163
Brief Announcement: Give Me Some Slack: Efficient Network Measurements

Authors: Ran Ben Basat, Gil Einziger, and Roy Friedman

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

Abstract
Many networking applications require timely access to recent network measurements, which can be captured using a sliding window model. Maintaining such measurements is a challenging task due to the fast line speed and scarcity of fast memory in routers. In this work, we study the impact of allowing slack in the window size on the asymptotic requirements of sliding window problems. That is, the algorithm can dynamically adjust the window size between W and W(1+tau) where tau is a small positive parameter. We demonstrate this model's attractiveness by showing that it enables efficient algorithms to problems such as Maximum and General-Summing that require Omega(W) bits even for constant factor approximations in the exact sliding window model. Additionally, for problems that admit sub-linear approximation algorithms such as Basic-Summing and Count-Distinct, the slack model enables a further asymptotic improvement. The main focus of our paper [{Ben Basat} et al., 2017] is on the widely studied Basic-Summing problem of computing the sum of the last W integers from {0,1 ...,R} in a stream. While it is known that Omega(W log{R}) bits are needed in the exact window model, we show that approximate windows allow an exponential space reduction for constant tau. Specifically, for tau=Theta(1), we present a space lower bound of Omega(log(RW)) bits. Additionally, we show an Omega(log ({W/epsilon})) lower bound for RW epsilon additive approximations and a Omega(log ({W/epsilon})+log log{R}) bits lower bound for (1+epsilon) multiplicative approximations. Our work is the first to study this problem in the exact and additive approximation settings. For all settings, we provide memory optimal algorithms that operate in worst case constant time. This strictly improves on the work of [Mayur Datar et al., 2002] for (1+epsilon)-multiplicative approximation that requires O(epsilon^{-1} log ({RW})log log ({RW})) space and performs updates in O(log ({RW})) worst case time. Finally, we show asymptotic improvements for the Count-Distinct, General-Summing and Maximum problems.
Cite as

Ran Ben Basat, Gil Einziger, and Roy Friedman. Brief Announcement: Give Me Some Slack: Efficient Network Measurements. In 45th International Colloquium on Automata, Languages, and Programming (ICALP 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 107, pp. 163:1-163:5, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)

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@InProceedings{benbasat_et_al:LIPIcs.ICALP.2018.163,
  author =	{Ben Basat, Ran and Einziger, Gil and Friedman, Roy},
  title =	{{Brief Announcement: Give Me Some Slack: Efficient Network Measurements}},
  booktitle =	{45th International Colloquium on Automata, Languages, and Programming (ICALP 2018)},
  pages =	{163:1--163:5},
  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.2018.163},
  URN =		{urn:nbn:de:0030-drops-91672},
  doi =		{10.4230/LIPIcs.ICALP.2018.163},
  annote =	{Keywords: Streaming, Algorithms, Sliding window, Lower bounds}
}
Document
DOI: 10.4230/LIPIcs.SWAT.2016.11
Efficient Summing over Sliding Windows

Authors: Ran Ben Basat, Gil Einziger, Roy Friedman, and Yaron Kassner

Published in: LIPIcs, Volume 53, 15th Scandinavian Symposium and Workshops on Algorithm Theory (SWAT 2016)

Abstract
This paper considers the problem of maintaining statistic aggregates over the last W elements of a data stream. First, the problem of counting the number of 1's in the last W bits of a binary stream is considered. A lower bound of Omega(1/epsilon + log(W)) memory bits for Wepsilon-additive approximations is derived. This is followed by an algorithm whose memory consumption is O(1/epsilon + log(W)) bits, indicating that the algorithm is optimal and that the bound is tight. Next, the more general problem of maintaining a sum of the last W integers, each in the range of {0, 1, ..., R}, is addressed. The paper shows that approximating the sum within an additive error of RW epsilon can also be done using Theta(1/epsilon + log(W)) bits for epsilon = Omega(1/W). For epsilon = o(1/W), we present a succinct algorithm which uses B(1 + o(1)) bits, where B = Theta(W*log(1/(W*epsilon))) is the derived lower bound. We show that all lower bounds generalize to randomized algorithms as well. All algorithms process new elements and answer queries in O(1) worst-case time.
Cite as

Ran Ben Basat, Gil Einziger, Roy Friedman, and Yaron Kassner. Efficient Summing over Sliding Windows. In 15th Scandinavian Symposium and Workshops on Algorithm Theory (SWAT 2016). Leibniz International Proceedings in Informatics (LIPIcs), Volume 53, pp. 11:1-11:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2016)

Copy BibTex To Clipboard

@InProceedings{benbasat_et_al:LIPIcs.SWAT.2016.11,
  author =	{Ben Basat, Ran and Einziger, Gil and Friedman, Roy and Kassner, Yaron},
  title =	{{Efficient Summing over Sliding Windows}},
  booktitle =	{15th Scandinavian Symposium and Workshops on Algorithm Theory (SWAT 2016)},
  pages =	{11:1--11:14},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-011-8},
  ISSN =	{1868-8969},
  year =	{2016},
  volume =	{53},
  editor =	{Pagh, Rasmus},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SWAT.2016.11},
  URN =		{urn:nbn:de:0030-drops-60241},
  doi =		{10.4230/LIPIcs.SWAT.2016.11},
  annote =	{Keywords: Streaming, Statistics, Lower Bounds}
}
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