Near real-time estimation of end-to-end performance in converged fixed-mobile networks

Abstract

The independent operation of mobile and fixed network segments is one of the main barriers that prevents improving network performance while reducing capital expenditures coming from overprovisioning. In particular, a coordinated dynamic network operation of both network segments is essential to guarantee end-to-end Key Performance Indicators (KPI), on which new network services rely on. To achieve such dynamic operation, accurate estimation of endto-end KPIs is needed to trigger network reconfiguration before performance degrades. In this paper, we present a methodology to achieve an accurate, scalable, and predictive estimation of end-to-end KPIs with sub-second granularity near real-time in converged fixed-mobile networks. Specifically, we extend our CURSA-SQ methodology for mobile network traffic analysis, to enable converged fixed-mobile network operation. CURSA-SQ combines simulation and machine learning fueled with real network monitoring data. Numerical results validate the accuracy, robustness, and usability of the proposed CURSA-SQ methodology for converged fixed-mobile network scenarios.

Reinforcement Learning -based Autonomous Multilayer Network Operation

“…For numerical evaluation purposes, we have generated synthetic traffic flows for customer connection and vlinks according to the methodology and traffic models used in [4]- [5] . Using such generator, we emulated different number of traffic, generating more than 100,000 different samples to be collected and processed.…”

Cooperative Learning for Disaggregated Delay Modeling in Multidomain Networks

“…CURSA-SQ [32] was used as a simulation environment. The CURSA-SQ methodology was tuned with the experimental measurements in [12], and successfully used to reproduce realistic packet scenarios, including converged fixed-mobile [33] and time-sensitive networks [34].…”

End-to-End Intent-Based Networking

“…In parallel, other initiatives have proposed orchestration solutions considering e2e service creation and operations for the RAN, transport, and computing from edge to cloud [3]. Smart orchestration requires collecting and analyzing large amounts of data, not only related to RAN, transport, and computation Key Performance Indicators (KPI) [4], but also from the applications to deal with the committed Quality of Experience (QoE). A recent study anticipates that application and infrastructure monitoring tools will be augmented with ML capabilities over the next five years [5].…”