TY - GEN

T1 - O'zapft is

T2 - 2017 ACM SIGCOMM Workshop on Big Data Analytics and Machine Learning for Data Communication Networks, Big-DAMA 2017

AU - Blenk, Andreas

AU - Kalmbach, Patrick

AU - Kellerer, Wolfgang

AU - Schmid, Stefan

N1 - Publisher Copyright:
© 2017 Copyright held by the owner/author(s).

PY - 2017/8/7

Y1 - 2017/8/7

N2 - 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.

AB - 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.

KW - Algorithms

KW - Big data

KW - Computer networks

KW - Machine learning

UR - http://www.scopus.com/inward/record.url?scp=85029491204&partnerID=8YFLogxK

U2 - 10.1145/3098593.3098597

DO - 10.1145/3098593.3098597

M3 - Conference contribution

AN - SCOPUS:85029491204

T3 - Big-DAMA 2017 - Proceedings of the 2017 Workshop on Big Data Analytics and Machine Learning for Data Communication Networks, Part of SIGCOMM 2017

SP - 19

EP - 24

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

PB - Association for Computing Machinery, Inc

Y2 - 21 August 2017

ER -