Facing Network Management Challenges with Functional Data Analysis: Techniques & Opportunities

Facing Network Management Challenges with Functional Data Analysis: Techniques & Opportunities Current fixed and mobile networks’ behavior is rapidly changing, which calls for flexible monitoring approaches to avoid loosing track with such a fast evolutionary pace. Due to the many challenges that this scenario is posing to network managers, we propose the exploration of Functional Data Analysis (FDA) techniques as a mean to easily deal with network management and analysis issues. Specifically, we describe and evaluate several FDA methods with applications to network measurement preprocessing and clustering, bandwidth allocation, and anomaly and outlier detection. Our work focuses on how these FDA-based tools serve to improve the outcomes of traffic data mining and analysis, providing easy-to-understand and comprehensive outputs for network managers. We present the results that we have obtained from real case studies in the Spanish Academic network using throughput time series, comparing them with other alternatives of the state of the art. With this com- parative, we have qualitatively and quantitatively evaluated the advantages of FDA-methods in the networking area. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Mobile Networks and Applications Springer Journals

Facing Network Management Challenges with Functional Data Analysis: Techniques & Opportunities

Facing Network Management Challenges with Functional Data Analysis: Techniques & Opportunities

Mobile Netw Appl (2017) 22:1124–1136 DOI 10.1007/s11036-016-0733-5 Facing Network Management Challenges with Functional Data Analysis: Techniques & Opportunities 1 1 2 1 David Muelas · Jorge E. Lopez ´ de Vergara · Jose´ R. Berrendero · Javier Ramos · Javier Aracil Published online: 4 May 2016 © Springer Science+Business Media New York 2016 Abstract Current fixed and mobile networks’ behavior 1 Introduction is rapidly changing, which calls for flexible monitoring approaches to avoid loosing track with such a fast evolu- Nowadays, network management is suffering an important tionary pace. Due to the many challenges that this scenario transformation as a result of the evolution of both the users’ is posing to network managers, we propose the exploration requirements and the deployed technologies. The use of of Functional Data Analysis (FDA) techniques as a mean to new communication services and infrastructures is chang- easily deal with network management and analysis issues. ing the approaches that Internet Service Providers (ISPs) Specifically, we describe and evaluate several FDA methods follow to maintain and monitor their networks. This fact, which is inherent to rapidly changing network dynamics, with applications to network measurement preprocessing and clustering, bandwidth allocation, and anomaly and out- entails that traditional measurement and analysis methods may easily become not flexible and adaptable enough. Thus, lier detection. Our work focuses on how these FDA-based tools serve to improve the outcomes of traffic data mining approaches based on particular statistical assumptions, such as specific marginal distributions or stationary processes, and analysis, providing easy-to-understand and comprehen- sive outputs for network managers. We present the results are useless in deployment scenarios where measurements present a different behavior —e.g., data Gaussianity is the that...
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Publisher
Springer Journals
Copyright
Copyright © 2016 by Springer Science+Business Media New York
Subject
Engineering; Communications Engineering, Networks; Computer Communication Networks; Electrical Engineering; IT in Business
ISSN
1383-469X
eISSN
1572-8153
D.O.I.
10.1007/s11036-016-0733-5
Publisher site
See Article on Publisher Site

Abstract

Current fixed and mobile networks’ behavior is rapidly changing, which calls for flexible monitoring approaches to avoid loosing track with such a fast evolutionary pace. Due to the many challenges that this scenario is posing to network managers, we propose the exploration of Functional Data Analysis (FDA) techniques as a mean to easily deal with network management and analysis issues. Specifically, we describe and evaluate several FDA methods with applications to network measurement preprocessing and clustering, bandwidth allocation, and anomaly and outlier detection. Our work focuses on how these FDA-based tools serve to improve the outcomes of traffic data mining and analysis, providing easy-to-understand and comprehensive outputs for network managers. We present the results that we have obtained from real case studies in the Spanish Academic network using throughput time series, comparing them with other alternatives of the state of the art. With this com- parative, we have qualitatively and quantitatively evaluated the advantages of FDA-methods in the networking area.

Journal

Mobile Networks and ApplicationsSpringer Journals

Published: May 4, 2016

References

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