Resumé
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 | |
| 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 |
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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
TY - GEN
T1 - NetSlicer: Automated and Traffic-Pattern Based Application Clustering in Datacenters
AU - Schiff, Liron
AU - Ziv, Ofri
AU - Jaeger, Manfred
AU - Schmid, Stefan
PY - 2018/8/7
Y1 - 2018/8/7
N2 - 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.
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.
KW - Big data
KW - Computer networks
KW - Machine learning
UR - http://www.scopus.com/inward/record.url?scp=85056412507&partnerID=8YFLogxK
U2 - 10.1145/3229607.3229614
DO - 10.1145/3229607.3229614
M3 - Article in proceeding
SP - 21
EP - 26
BT - Big-DAMA 2018 - Proceedings of the 2018 Workshop on Big Data Analytics and Machine Learning for Data Communication Networks, Part of SIGCOMM 2018
PB - Association for Computing Machinery
ER -