IntOpt: in-band network telemetry optimization framework to monitor network slices using P4

Article


Bhamare, D., Kassler, A., Vestin, J., Khoshkholghi, A., Taheri, J., Mahmoodi, T., Ohlen, P. and Curescu, C. 2022. IntOpt: in-band network telemetry optimization framework to monitor network slices using P4. Computer Networks. 216. https://doi.org/10.1016/j.comnet.2022.109214
TypeArticle
TitleIntOpt: in-band network telemetry optimization framework to monitor network slices using P4
AuthorsBhamare, D., Kassler, A., Vestin, J., Khoshkholghi, A., Taheri, J., Mahmoodi, T., Ohlen, P. and Curescu, C.
Abstract

The emergence of Network Functions Virtualization (NFV) is being heralded as an enabler of the recent technologies such as 5G/6G, IoT and heterogeneous networks. Existing NFV monitoring frameworks either do not have the capabilities to express the range of telemetry items needed to perform management or do not scale to large traffic volumes and rates. We present IntOpt, a scalable and expressive telemetry system designed for flexible NFV monitoring using active probing and P4. IntOpt allows us to specify monitoring requirements for individual service chain, which are mapped to telemetry item collection jobs that fetch the required telemetry items from P4 programmable data-plane elements. We propose mixed integer linear program (MILP) as well as a simulated annealing based random greedy (SARG) meta-heuristic approach to minimize the overhead due to active probing and collection of telemetry items. Using P4-FPGA, we benchmark the overhead for telemetry collection. Our numerical evaluation shows that the proposed approach can reduce monitoring overheads by 39% and monitoring delays by 57%. Such optimization may as well enable existing expressive monitoring frameworks to scale for larger real-time networks.

KeywordsIn-band Network Telemetry (INT); Monitoring; P4; Network function virtualization (NFV); Service function chain (SFC)
Sustainable Development Goals9 Industry, innovation and infrastructure
Middlesex University ThemeCreativity, Culture & Enterprise
PublisherElsevier
JournalComputer Networks
ISSN1389-1286
Electronic1872-7069
Publication dates
Online03 Aug 2022
Print24 Oct 2022
Publication process dates
Submitted10 Mar 2022
Accepted21 Jul 2022
Deposited12 Sep 2024
Output statusPublished
Publisher's version
License
File Access Level
Open
Copyright Statement

© 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Digital Object Identifier (DOI)https://doi.org/10.1016/j.comnet.2022.109214
Scopus EID2-s2.0-85135954926
Web of Science identifierWOS:000866230500014
LanguageEnglish
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