Uncertainty-driven Ensemble Forecasting of QoS in Software Defined Networks

Kolomvatsos, K., Anagnostopoulos, C., Marnerides, A. K., Ni, Q., Hadjiefthymiades, S. and Pezaros, D. (2017) Uncertainty-driven Ensemble Forecasting of QoS in Software Defined Networks. In: 22nd IEEE Symposium on Computers and Communication (ISCC 2017), Heraklion Crete, Greece, 03-06 Jul 2017, (doi:10.1109/ISCC.2017.8024701)

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Abstract

Software Defined Networking (SDN) is the key technology for combining networking and Cloud solutions to provide novel applications. SDN offers a number of advantages as the existing resources can be virtualized and orchestrated to provide new services to the end users. Such a technology should be accompanied by powerful mechanisms that ensure the end-to- end quality of service at high levels, thus, enabling support for complex applications that satisfy end users needs. In this paper, we propose an intelligent mechanism that agglomerates the benefits of SDNs with real-time ‘Big Data’ forecasting analytics. The proposed mechanism, as part of the SDN controller, supports predictive intelligence by monitoring a set of network performance parameters, forecasting their future values, and deriving indications on potential service quality violations. By treating the performance measurements as time-series, our mechanism employs a novel ensemble forecasting methodology to estimate their future values. Such predictions are fed to a Type-2 Fuzzy Logic system to deliver, in real-time, decisions related to service quality violations. Such decisions proactively assist the SDN controller for providing the best possible orchestration of the virtualized resources. We evaluate the proposed mechanism w.r.t. precision and recall metrics over synthetic data.

Item Type:Conference Proceedings
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Anagnostopoulos, Dr Christos and Pezaros, Dr Dimitrios
Authors: Kolomvatsos, K., Anagnostopoulos, C., Marnerides, A. K., Ni, Q., Hadjiefthymiades, S., and Pezaros, D.
College/School:College of Science and Engineering > School of Computing Science
ISSN:9781538616291
Copyright Holders:Copyright © 2017 IEEE
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher.
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