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 proceedingKonferenceartikel i proceedingForskningpeer 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.
OriginalsprogEngelsk
TitelBig-DAMA 2018 - Proceedings of the 2018 Workshop on Big Data Analytics and Machine Learning for Data Communication Networks, Part of SIGCOMM 2018
Antal sider6
ForlagAssociation for Computing Machinery
Publikationsdato7 aug. 2018
Sider21-26
ISBN (Elektronisk)978-1-4503-5904-7
DOI
StatusUdgivet - 7 aug. 2018
Begivenhed2018 Workshop on Big Data Analytics and Machine Learning for Data Communication Networks: Big-DAMA - Budapest, Ungarn
Varighed: 20 aug. 201820 aug. 2018

Konference

Konference2018 Workshop on Big Data Analytics and Machine Learning for Data Communication Networks
LandUngarn
ByBudapest
Periode20/08/201820/08/2018

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