Traffic matrices, abstract representations of demand, are essential for network operators endeavoring to model, measure, maintain, and improve the efficiency of their complex and heterogeneous architectures. Traffic matrix estimation consists in inferring a traffic matrix from link-level measurements. Provoked by the need to enable agile deployment of new services while, at the same time, slashing operating expenditure and energy consumption, the trend in telecommunications is to shift functionality from physical appliances to virtualized services. We analyze the effects of this landscape change on traffic matrices, their dynamics, and their estimation, indicating some new challenges and problems that will arise in all the associated modeling, analysis and evaluation activities.
|Titolo:||Traffic matrix estimation with software-defined NFV: Challenges and opportunities|
|Autori interni:||FIORE, UGO|
|Data di pubblicazione:||2017|
|Rivista:||JOURNAL OF COMPUTATIONAL SCIENCE|
|Appare nelle tipologie:||1.1 Articolo in rivista|