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Empowering Self-Driving Networks

Authors:

Patrick Kalmbach,

Johannes Zerwas,

Péter Babarczi,

Wolfgang Kellerer,

Stefan SchmidAuthors Info & Claims

SelfDN 2018: Proceedings of the Afternoon Workshop on Self-Driving Networks

Pages 8 - 14

https://doi.org/10.1145/3229584.3229587

Published: 07 August 2018 Publication History

Abstract

As emerging network technologies and softwareization render networks more flexible, the question arises of how to exploit these flexibilities for optimization. Given the complexity of the involved network protocols and the context in which networks are operating, such optimizations are increasingly difficult to perform. An interesting vision in this regard are "self-driving" networks: networks which measure, analyze and control themselves in an automated manner, reacting to changes in the environment (e.g., demand), while exploiting existing flexibilities to optimize themselves.

A fundamental challenge faced by any (self-)optimizing network concerns the limited knowledge about future changes in the demand and environment in which the network is operating. Indeed, given that reconfigurations entail resource costs and may take time, an "optimal" network configuration for the current demand and environment may not necessarily be optimal also in the near future. Thus, it is desirable that (self-)optimizations also prepare the network for possibly unexpected events.

This paper makes the case for empowering self-driving networks: empowerment is an information-centric measure which accounts for how "prepared" a network is and how much flexibility is preserved over time. While empowerment has been successfully employed in other domains such as robotics, we are not aware of any applications in networking. As a case study for the use of empowerment in networks, we consider self-driving networks offering topological flexibilities, i.e., reconfigurable edges.

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cover image ACM Conferences

SelfDN 2018: Proceedings of the Afternoon Workshop on Self-Driving Networks

August 2018

48 pages

ISBN:9781450359146

DOI:10.1145/3229584

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 07 August 2018

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H2020 European Research Council

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SIGCOMM '18

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SIGCOMM

SIGCOMM '18: ACM SIGCOMM 2018 Conference

August 24, 2018

Budapest, Hungary

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https://doi.org/10.1109/TMLCN.2024.3501217
Akem ABütün BGucciardo MFiore M(2024)Jewel: Resource-Efficient Joint Packet and Flow Level Inference in Programmable SwitchesIEEE INFOCOM 2024 - IEEE Conference on Computer Communications10.1109/INFOCOM52122.2024.10621365(1631-1640)Online publication date: 20-May-2024
https://doi.org/10.1109/INFOCOM52122.2024.10621365
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https://dl.acm.org/doi/10.1007/s10922-024-09835-7
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