NetSlicer: Automated and Traffic-Pattern Based Application Clustering in Datacenters

Liron Schiff; Ofri Ziv; Manfred Jaeger; Stefan Schmid

doi:10.1145/3229607.3229614

NetSlicer: Automated and Traffic-Pattern Based Application Clustering in Datacenters

Liron Schiff, Ofri Ziv, Manfred Jaeger, Stefan Schmid

Publikation: Bidrag til bog/antologi/rapport/konference proceeding › Konferenceartikel i proceeding › Forskning › peer review

2 Citationer (Scopus)

Abstrakt

Companies often have very limited information about the applications running in their datacenter or public/private cloud environments. As this can harm efficiency, performance, and security, many
network administrators work hard to manually assign actionable
description to (virtual) machines.
This paper presents and evaluates NetSlicer , a machine-learning
approach that enables an automated grouping of nodes into applications and their tiers. Our solution is based solely on the available
network layer data which is used as part of a novel graph clustering
algorithm, tailored toward the datacenter use case and accounting
also for observed port numbers. For the sake of this paper, we also
performed an extensive empirical measurement study, collecting
actual workloads from different production datacenters (data to
be released together with this paper). We find that our approach
features a high accuracy.

Originalsprog	Engelsk
Titel	Big-DAMA 2018 - Proceedings of the 2018 Workshop on Big Data Analytics and Machine Learning for Data Communication Networks, Part of SIGCOMM 2018
Antal sider	6
Forlag	Association for Computing Machinery
Publikationsdato	7 aug. 2018
Sider	21-26
ISBN (Elektronisk)	978-1-4503-5904-7
DOI	https://doi.org/10.1145/3229607.3229614
Status	Udgivet - 7 aug. 2018
Begivenhed	2018 Workshop on Big Data Analytics and Machine Learning for Data Communication Networks: Big-DAMA - Budapest, Ungarn Varighed: 20 aug. 2018 → 20 aug. 2018

Konference

Konference	2018 Workshop on Big Data Analytics and Machine Learning for Data Communication Networks
Land	Ungarn
By	Budapest
Periode	20/08/2018 → 20/08/2018

Adgang til dokumentet

10.1145/3229607.3229614

Andre filer og links

http://www.scopus.com/inward/record.url?scp=85056412507&partnerID=8YFLogxK

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Citationsformater

Schiff, L., Ziv, O., Jaeger, M., & Schmid, S. (2018). NetSlicer: Automated and Traffic-Pattern Based Application Clustering in Datacenters. I Big-DAMA 2018 - Proceedings of the 2018 Workshop on Big Data Analytics and Machine Learning for Data Communication Networks, Part of SIGCOMM 2018 (s. 21-26). Association for Computing Machinery. https://doi.org/10.1145/3229607.3229614

@inproceedings{1084506ad75b4e9e9214421380ee7e5c,

title = "NetSlicer: Automated and Traffic-Pattern Based Application Clustering in Datacenters",

abstract = "Companies often have very limited information about the applications running in their datacenter or public/private cloud environments. As this can harm efficiency, performance, and security, manynetwork administrators work hard to manually assign actionabledescription to (virtual) machines.This paper presents and evaluates NetSlicer , a machine-learningapproach that enables an automated grouping of nodes into applications and their tiers. Our solution is based solely on the availablenetwork layer data which is used as part of a novel graph clusteringalgorithm, tailored toward the datacenter use case and accountingalso for observed port numbers. For the sake of this paper, we alsoperformed an extensive empirical measurement study, collectingactual workloads from different production datacenters (data tobe released together with this paper). We find that our approachfeatures a high accuracy.",

keywords = "Big data, Computer networks, Machine learning",

author = "Liron Schiff and Ofri Ziv and Manfred Jaeger and Stefan Schmid",

year = "2018",

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doi = "10.1145/3229607.3229614",

language = "English",

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booktitle = "Big-DAMA 2018 - Proceedings of the 2018 Workshop on Big Data Analytics and Machine Learning for Data Communication Networks, Part of SIGCOMM 2018",

publisher = "Association for Computing Machinery",

address = "United States",

note = "2018 Workshop on Big Data Analytics and Machine Learning for Data Communication Networks : Big-DAMA ; Conference date: 20-08-2018 Through 20-08-2018",

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Schiff, L, Ziv, O, Jaeger, M & Schmid, S 2018, NetSlicer: Automated and Traffic-Pattern Based Application Clustering in Datacenters. i Big-DAMA 2018 - Proceedings of the 2018 Workshop on Big Data Analytics and Machine Learning for Data Communication Networks, Part of SIGCOMM 2018. Association for Computing Machinery, s. 21-26, 2018 Workshop on Big Data Analytics and Machine Learning for Data Communication Networks, Budapest, Ungarn, 20/08/2018. https://doi.org/10.1145/3229607.3229614

NetSlicer: Automated and Traffic-Pattern Based Application Clustering in Datacenters. / Schiff, Liron; Ziv, Ofri; Jaeger, Manfred; Schmid, Stefan.

Big-DAMA 2018 - Proceedings of the 2018 Workshop on Big Data Analytics and Machine Learning for Data Communication Networks, Part of SIGCOMM 2018. Association for Computing Machinery, 2018. s. 21-26.

Publikation: Bidrag til bog/antologi/rapport/konference proceeding › Konferenceartikel i proceeding › Forskning › peer review

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AU - Schiff, Liron

AU - Ziv, Ofri

AU - Jaeger, Manfred

AU - Schmid, Stefan

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AB - Companies often have very limited information about the applications running in their datacenter or public/private cloud environments. As this can harm efficiency, performance, and security, manynetwork administrators work hard to manually assign actionabledescription to (virtual) machines.This paper presents and evaluates NetSlicer , a machine-learningapproach that enables an automated grouping of nodes into applications and their tiers. Our solution is based solely on the availablenetwork layer data which is used as part of a novel graph clusteringalgorithm, tailored toward the datacenter use case and accountingalso for observed port numbers. For the sake of this paper, we alsoperformed an extensive empirical measurement study, collectingactual workloads from different production datacenters (data tobe released together with this paper). We find that our approachfeatures a high accuracy.

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KW - Computer networks

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