O'zapft is: Tap your network algorithm's big data!

Andreas Blenk, Patrick Kalmbach, Wolfgang Kellerer, Stefan Schmid

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

28 Scopus citations

Abstract

At the heart of many computer network planning, deployment, and operational tasks lie hard algorithmic problems. Accordingly, over the last decades, we have witnessed a continuous pursuit for ever more accurate and faster algorithms. We propose an approach to design network algorithms which is radically different from most existing algorithms. Our approach is motivated by the observation that most existing algorithms to solve a given hard computer networking problem overlook a simple yet very powerful optimization opportunity in practice: many network algorithms are executed repeatedly (e.g., for each virtual network request or in reaction to user mobility), and hence with each execution, generate interesting data: (problem, solution)-pairs. We make the case for leveraging the potentially big data of an algorithm's past executions to improve and speed up future, similar solutions, by reducing the algorithm's search space.We study the applicability of machine learning to network algorithm design, identify challenges and discuss limitations. We empirically demonstrate the potential of machine learning network algorithms in two case studies, namely the embedding of virtual networks (a packing optimization problem) and k-center facility location (a covering optimization problem), using a prototype implementation.

Original languageEnglish
Title of host publicationBig-DAMA 2017 - Proceedings of the 2017 Workshop on Big Data Analytics and Machine Learning for Data Communication Networks, Part of SIGCOMM 2017
PublisherAssociation for Computing Machinery, Inc
Pages19-24
Number of pages6
ISBN (Electronic)9781450350549
DOIs
StatePublished - 7 Aug 2017
Event2017 ACM SIGCOMM Workshop on Big Data Analytics and Machine Learning for Data Communication Networks, Big-DAMA 2017 - Los Angeles, United States
Duration: 21 Aug 2017 → …

Publication series

NameBig-DAMA 2017 - Proceedings of the 2017 Workshop on Big Data Analytics and Machine Learning for Data Communication Networks, Part of SIGCOMM 2017

Conference

Conference2017 ACM SIGCOMM Workshop on Big Data Analytics and Machine Learning for Data Communication Networks, Big-DAMA 2017
Country/TerritoryUnited States
CityLos Angeles
Period21/08/17 → …

Keywords

  • Algorithms
  • Big data
  • Computer networks
  • Machine learning

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