DCAD: Dynamic cell anomaly detection for operational cellular networks

Gabriela Ciocarlie; Ulf Lindqvist; Kenneth Nitz; Szabolcs Nováczki; Henning Sanneck

doi:10.1109/NOMS.2014.6838271

DCAD: Dynamic cell anomaly detection for operational cellular networks

Gabriela Ciocarlie
, Ulf Lindqvist
, Kenneth Nitz
, Szabolcs Nováczki
, Henning Sanneck

Department of Computer Science

Stevens Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

The Self-Organizing Networks (SON) concept includes the functional area known as self-healing, which aims to automate the detection and diagnosis of, and recovery from, network degradations and outages. In this paper, we present Dynamic Cell Anomaly Detection (DCAD), a tool that implements an adaptive ensemble method for modeling cell behavior [5], [6]. DCAD uses Key Performance Indicators (KPIs) from real cellular networks to determine cell-performance status; enables KPI data exploration; visualizes anomalies; reduces the time required for successful detection of anomalies; and accepts user input.

Original language	English
Title of host publication	IEEE/IFIP NOMS 2014 - IEEE/IFIP Network Operations and Management Symposium
Subtitle of host publication	Management in a Software Defined World
Publisher	IEEE Computer Society
ISBN (Print)	9781479909131
DOIs	https://doi.org/10.1109/NOMS.2014.6838271
State	Published - 2014
Event	IEEE/IFIP Network Operations and Management Symposium: Management in a Software Defined World, NOMS 2014 - Krakow, Poland Duration: May 5 2014 → May 9 2014

Publication series

Name	IEEE/IFIP NOMS 2014 - IEEE/IFIP Network Operations and Management Symposium: Management in a Software Defined World

Conference

Conference	IEEE/IFIP Network Operations and Management Symposium: Management in a Software Defined World, NOMS 2014
Country/Territory	Poland
City	Krakow
Period	5/5/14 → 5/9/14

ASJC Scopus subject areas

Computer Networks and Communications

Keywords

Key Performance Indicators (KPIs)
Self-Healing
Self-Organizing Networks (SON)
cell anomaly detection
performance management

Access to Document

10.1109/NOMS.2014.6838271

Cite this

Ciocarlie, G., Lindqvist, U., Nitz, K., Nováczki, S., & Sanneck, H. (2014). DCAD: Dynamic cell anomaly detection for operational cellular networks. In IEEE/IFIP NOMS 2014 - IEEE/IFIP Network Operations and Management Symposium: Management in a Software Defined World Article 6838271 (IEEE/IFIP NOMS 2014 - IEEE/IFIP Network Operations and Management Symposium: Management in a Software Defined World). IEEE Computer Society. https://doi.org/10.1109/NOMS.2014.6838271

Ciocarlie, Gabriela ; Lindqvist, Ulf ; Nitz, Kenneth et al. / DCAD : Dynamic cell anomaly detection for operational cellular networks. IEEE/IFIP NOMS 2014 - IEEE/IFIP Network Operations and Management Symposium: Management in a Software Defined World. IEEE Computer Society, 2014. (IEEE/IFIP NOMS 2014 - IEEE/IFIP Network Operations and Management Symposium: Management in a Software Defined World).

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title = "DCAD: Dynamic cell anomaly detection for operational cellular networks",

abstract = "The Self-Organizing Networks (SON) concept includes the functional area known as self-healing, which aims to automate the detection and diagnosis of, and recovery from, network degradations and outages. In this paper, we present Dynamic Cell Anomaly Detection (DCAD), a tool that implements an adaptive ensemble method for modeling cell behavior [5], [6]. DCAD uses Key Performance Indicators (KPIs) from real cellular networks to determine cell-performance status; enables KPI data exploration; visualizes anomalies; reduces the time required for successful detection of anomalies; and accepts user input.",

keywords = "Key Performance Indicators (KPIs), Self-Healing, Self-Organizing Networks (SON), cell anomaly detection, performance management",

author = "Gabriela Ciocarlie and Ulf Lindqvist and Kenneth Nitz and Szabolcs Nov{\'a}czki and Henning Sanneck",

year = "2014",

doi = "10.1109/NOMS.2014.6838271",

language = "English",

isbn = "9781479909131",

series = "IEEE/IFIP NOMS 2014 - IEEE/IFIP Network Operations and Management Symposium: Management in a Software Defined World",

publisher = "IEEE Computer Society",

booktitle = "IEEE/IFIP NOMS 2014 - IEEE/IFIP Network Operations and Management Symposium",

note = "IEEE/IFIP Network Operations and Management Symposium: Management in a Software Defined World, NOMS 2014 ; Conference date: 05-05-2014 Through 09-05-2014",

}

Ciocarlie, G, Lindqvist, U, Nitz, K, Nováczki, S & Sanneck, H 2014, DCAD: Dynamic cell anomaly detection for operational cellular networks. in IEEE/IFIP NOMS 2014 - IEEE/IFIP Network Operations and Management Symposium: Management in a Software Defined World., 6838271, IEEE/IFIP NOMS 2014 - IEEE/IFIP Network Operations and Management Symposium: Management in a Software Defined World, IEEE Computer Society, IEEE/IFIP Network Operations and Management Symposium: Management in a Software Defined World, NOMS 2014, Krakow, Poland, 5/5/14. https://doi.org/10.1109/NOMS.2014.6838271

DCAD: Dynamic cell anomaly detection for operational cellular networks. / Ciocarlie, Gabriela; Lindqvist, Ulf; Nitz, Kenneth et al.
IEEE/IFIP NOMS 2014 - IEEE/IFIP Network Operations and Management Symposium: Management in a Software Defined World. IEEE Computer Society, 2014. 6838271 (IEEE/IFIP NOMS 2014 - IEEE/IFIP Network Operations and Management Symposium: Management in a Software Defined World).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - DCAD

T2 - IEEE/IFIP Network Operations and Management Symposium: Management in a Software Defined World, NOMS 2014

AU - Ciocarlie, Gabriela

AU - Lindqvist, Ulf

AU - Nitz, Kenneth

AU - Nováczki, Szabolcs

AU - Sanneck, Henning

PY - 2014

Y1 - 2014

N2 - The Self-Organizing Networks (SON) concept includes the functional area known as self-healing, which aims to automate the detection and diagnosis of, and recovery from, network degradations and outages. In this paper, we present Dynamic Cell Anomaly Detection (DCAD), a tool that implements an adaptive ensemble method for modeling cell behavior [5], [6]. DCAD uses Key Performance Indicators (KPIs) from real cellular networks to determine cell-performance status; enables KPI data exploration; visualizes anomalies; reduces the time required for successful detection of anomalies; and accepts user input.

AB - The Self-Organizing Networks (SON) concept includes the functional area known as self-healing, which aims to automate the detection and diagnosis of, and recovery from, network degradations and outages. In this paper, we present Dynamic Cell Anomaly Detection (DCAD), a tool that implements an adaptive ensemble method for modeling cell behavior [5], [6]. DCAD uses Key Performance Indicators (KPIs) from real cellular networks to determine cell-performance status; enables KPI data exploration; visualizes anomalies; reduces the time required for successful detection of anomalies; and accepts user input.

KW - Key Performance Indicators (KPIs)

KW - Self-Healing

KW - Self-Organizing Networks (SON)

KW - cell anomaly detection

KW - performance management

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DO - 10.1109/NOMS.2014.6838271

M3 - Conference contribution

SN - 9781479909131

T3 - IEEE/IFIP NOMS 2014 - IEEE/IFIP Network Operations and Management Symposium: Management in a Software Defined World

BT - IEEE/IFIP NOMS 2014 - IEEE/IFIP Network Operations and Management Symposium

PB - IEEE Computer Society

Y2 - 5 May 2014 through 9 May 2014

ER -

Ciocarlie G, Lindqvist U, Nitz K, Nováczki S, Sanneck H. DCAD: Dynamic cell anomaly detection for operational cellular networks. In IEEE/IFIP NOMS 2014 - IEEE/IFIP Network Operations and Management Symposium: Management in a Software Defined World. IEEE Computer Society. 2014. 6838271. (IEEE/IFIP NOMS 2014 - IEEE/IFIP Network Operations and Management Symposium: Management in a Software Defined World). doi: 10.1109/NOMS.2014.6838271

DCAD: Dynamic cell anomaly detection for operational cellular networks

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ASJC Scopus subject areas

Keywords

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