Cloud-RAN Factory: Instantiating virtualized mobile networks with ONAP

In this demo, we exhibit the negotiation based on the TM Forum framework (Customer Facing Service and Resource Facing Service) and the deployment of a fully virtualized end-to-end mobile network (including a RAN desegregated into Remote Unit, Distributed Unit and Centralized Unit) by using ONAP, an open-source network automation platform. The various components of mobile network are containerized and deployed by ONAP on top of Kubernetes. The demo is the first illustration of an end-to-end mobile network, which is fully virtualized up to the remote unit, whose architecture is compatible with the Open-RAN framework, and which implements a PHY layer on the basis of 3GPP 7.3 functional split in Open Air Interface code.Comment: Demo paper presenter at NoF 2020 conferenc

Rethinking Buffer Status Estimation to Improve Radio Resource Utilization in Cellular Networks

International audienceIn LTE and 5G mobile networks, radio resources are dynamically allocated. In the uplink, the Base Station (BS) needs to know how much data each User equipement (UE) has in its butter to properly share the resource among the different UEs. In this paper, using a simple scenario and measurements made on an operational network, we show that the BS works on systematically outdated information about the butter size, which generates delay spikes. We thus propose to add a predictive estimation of the butter size, called enhanced Buffer Status Report (eBSR). It works with off-the-shelf UE and is incorporated in BS at the Medium Access Control (MAC) layer without changes in scheduling algorithm. We evaluated it on a lab testbed with various traffic profiles. Results show a clear improvement on uplink latency and jitter, together with a limited impact on radio resource usage efficiency

Rethinking Buffer Status Estimation to Improve Radio Resource Utilization in Cellular Networks

International audienceIn LTE and 5G mobile networks, radio resources are dynamically allocated. In the uplink, the Base Station (BS) needs to know how much data each User equipement (UE) has in its butter to properly share the resource among the different UEs. In this paper, using a simple scenario and measurements made on an operational network, we show that the BS works on systematically outdated information about the butter size, which generates delay spikes. We thus propose to add a predictive estimation of the butter size, called enhanced Buffer Status Report (eBSR). It works with off-the-shelf UE and is incorporated in BS at the Medium Access Control (MAC) layer without changes in scheduling algorithm. We evaluated it on a lab testbed with various traffic profiles. Results show a clear improvement on uplink latency and jitter, together with a limited impact on radio resource usage efficiency

Automated Identification of BBR Traffic based on Packet Inter-Arrival Times Analysis

International audienceThe Internet is a complex and constantly evolving system, and congestion control algorithms play a crucial role in ensuring its functioning by managing network performance. These algorithms regulate the flow of data within a network and optimize data transmission for efficiency and effectiveness. They do this by continuously estimating available network resources and adjusting the data transmission rate accordingly. For network operators, identifying the congestion control algorithms being used on their network is essential to gain valuable insights into network performance and device behavior. This information can help them gain a better understanding of how the network is being utilized and which algorithms are most effective in different scenarios. With a clear understanding of the congestion control algorithms in use, they can make decisions about network design, configuration, and management. Nowadays, over 85\% of total Internet traffic is TCP traffic. TCP uses different congestion control algorithms, of which BBR and CUBIC represent 73\% of the total TCP traffic. In this work, we present a method for automatically identifying BBR traffic on the Internet. Our method relies on analyzing packet inter-arrival times, specifically comparing the distribution of packet inter-arrival times during the Slow-Start state of a BBR capture with those of a CUBIC capture. We introduce a model that allows us to detect the silence period after packet bursts that are present in almost all non-BBR congestion control algorithms. This method is characterized by a very simple frontend signal processing that exploits the algorithms' core principles, allowing for a tiny parameter space dimension (two), which is sufficient for robust discrimination: an error rate of 4.1\% was obtained on a test dataset independent from training

Automated slow-start detection for anomaly root cause analysis and BBR identification

International audienceNetworks troubleshooting usually requires packet level traffic capturing and analysing. Indeed, the observation of emission patterns sheds some light on the kind of degradation experienced by a connection. In the case of reliable transport traffic where congestion control is performed, such as TCP and QUIC traffic, these patterns are the fruit of decisions made by the Congestion Control Algorithm (CCA), according to its own perception of network conditions. The CCA estimates the bottleneck's capacity via an exponential probing, during the so-called "Slow-Start" (SS) state. The bottleneck is considered as reached upon reception of congestion signs, typically lost packets or abnormal packet delays depending on the version of CCA used. The SS state duration is thus a key indicator for the diagnosis of faults; this indicator is estimated empirically by human experts today, which is time-consuming and a cumbersome task with large error margins. This paper proposes a method to automatically identify the Slow-Start state from actively and passively obtained bidirectional packet traces. It relies on an innovative timeless representation of the observed packets series. We implemented our method in our active and passive probes and tested it with CUBIC and BBR under different network conditions. We then picked a few real-life examples to illustrate the value of our representation for easy discrimination between typical faults and for identifying BBR among CCAs variants

Troubleshooting Enhancement with Automated Slow-Start Detection

International audienceDetecting anomalies in networks usually requires packet level traffic capturing and analysing. Indeed, the observation of emission patterns sheds some light on the kind of degradation experienced by a connection. In the case of reliable transport traffic where congestion control is performed, such as TCP and QUIC traffic, these patterns are the fruit of decisions made by the Congestion Control Algorithm (CCA), according to its own perception of network conditions. The CCA estimates the bottleneck's capacity via an exponential probing, during the so-called "Slow-Start" (SS) state. The bottleneck is considered as reached upon reception of congestion signs, typically lost packets or abnormal packet delays depending on the version of CCA used. The SS state duration is thus a key indicator for the diagnosis of faults; this indicator is estimated empirically by human experts today, which is time-consuming and a cumbersome task with large error margins. This paper proposes a method to automatically identify the Slow-Start state from actively and passively obtained bidirectional packet traces. It relies on an innovative timeless representation of the observed packets series. We implemented our method in our active and passive probes and tested it with CUBIC and BBR under different network conditions. We then picked a few real-life examples to illustrate the value of our representation for easy discrimination between typical faults

CDBE: A cooperative way to improve end-to-end congestion control in mobile network

International audienc

LatSeq, un outil de mesure des latences internes à l'OpenAirInterface

Demo of LatSeqInternational audienc

LatSeq, un outil de mesure des latences internes à l'OpenAirInterface

Demo of LatSeqInternational audienc