Authors:
Pedro A. Aranda Gutierrez
1
;
David Wagner
2
;
Ilka Miloucheva
2
;
Christof Brandauer
3
and
Ulrich Hofmann
3
Affiliations:
1
Telefonica R&D, Spain
;
2
Fraunhofer Institute, Schloss Birlinghoven, Germany
;
3
Salzburg Research, Austria
Keyword(s):
QoS measurement policy, policy repository, heterogeneous access IP network, learning component, reinforcement learning, supervised learning.
Related
Ontology
Subjects/Areas/Topics:
Mobile Software and Services
;
Telecommunication Software Systems, Tools and Languages
;
Telecommunications
;
Wireless Information Networks and Systems
Abstract:
A challenge of today’s measurement architectures for QoS/SLA monitoring in heterogeneous network environment is enhanced intelligence in order to minimise measurements and derive automatically optimised measurement strategies for the network operators. Such optimisations can be done with different goals – avoid redundant measurements, sharing of measurements for different QoS monitoring goals and enhancement of measurement strategies considering QoS/SLA measurement requests. For automated optimisation of measurement strategies, QoS measurement policies are proposed whose parameters are adapted dynamically based on specified learning algorithms and rules. For the policy adaptation different kinds of learning can be used, as for instance reinforcement and supervised learning. The integration of the proposed policy based strategies into policy management architecture is discussed. A learning component collecting rules and algorithms for measurement policy adaptation is proposed which ca
n be used by different tools of a policy management system. A graphical user interface (GUI) for a realistic policy based measurement scenario is discussed which aims to optimise the measurement strategies of the network operator.
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