Publicações

DMFD: Non-Intrusive Dependency Inference and Flow Ratio Model for Performance Anomaly Detection in Multi-Tier Cloud Applications

AuthID
P-00R-37M

Author(s)

Fu, SB

Prior, R

Kim, H

Editor(es)

Elisa Bertino; Carl K. Chang; Peter Chen; Ernesto Damiani; Michael Goul; Katsunori Oyama

Tipo de Documento

Proceedings Paper

Year published

2019

Publicado

in 2019 IEEE 12TH INTERNATIONAL CONFERENCE ON CLOUD COMPUTING (IEEE CLOUD 2019) in IEEE International Conference on Cloud Computing, ISSN: 2159-6182

Volume: 2019-July, Páginas: 164-173 (10)

Conference

12Th Ieee International Conference on Cloud Computing (Ieee Cloud) Held as Part of Ieee World Congress on Services (Ieee Services), Date: JUL 08-13, 2019, Location: MIlan, ITALY, Patrocinadores: IEEE, IEEE Comp Soc, CCF TCSC

Indexing

Wos^®

Scopus^®

Dblp^®/pt/publications/view/789748

Crossref^®1

Google Scholar^®

Metadata
Fontes

Publication Identifiers

DOI: 10.1109/cloud.2019.00036

DBLP: conf/IEEEcloud/FuPK19

SCOPUS: 2-s2.0-85072323339

Wos: WOS:000556208000023

Source Identifiers

ISSN: 2159-6182

Export Publication Metadata

Lista Marked

Adicionar à lista Marked

Info

At this moment we don't have any links to full text documens.

DMFD: Non-Intrusive Dependency Inference and Flow Ratio Model for Performance Anomaly Detection in Multi-Tier Cloud Applications

Links Úteis

Ajuda

Autenticação Federada (Clique na imagem)

DMFD: Non-Intrusive Dependency Inference and Flow Ratio Model for Performance Anomaly Detection in Multi-Tier Cloud Applications

Links Úteis

Ajuda