Program

Schedule

09:00 - 10:00: Session 1

Chair: Carlos Natalino, Chalmers University of Technology, Sweden

10:00 - 11:00: Keynote 1

Chair: Ricard Vilalta, Centre Tecnològic Telecomunicacions Catalunya, Spain

Network Slicing: intersection of 3GPP, O-RAN and IETF
Ivan Bykov

11:00 - 11:30: Coffee break

11:30 - 11:50: Session 2

Chair: Christian E. Rothenberg, University of Campinas, Brazil 

11:50 - 12:50: Keynote 2

Chair: Luis M. Contreras, Telefonica, Spain

ETSI TeraFlowSDN: an open and cloud-native SDN Controller for the 6G Era
Lluis Gifre

12:50 - 13:30 - Panel discussion

All presenters and session chairs

 

 

Keynotes

 

Network Slicing: intersection of 3GPP, O-RAN and IETF

Abstract: TBD

Ivan Bykov is a technical evangelist with over 18 years of experience in telecommunications. In Ribbon, Ivan is leading the company’s Mobile and Service Providers Solutions strategy. 

Prior to this, for many years Ivan has been leading the network architecture division of the largest mobile operator in Europe.

During his tenure as a network architect lead, Ivan Bykov has pioneered many successful nationwide and international projects, including building a Nation-Wide IP/MPLS Multi-Service network. Ivan also conceived and swiftly executed the entire operator’s network migration from 3G to LTE, further adding to the operator’s success by competing performance-critical projects such as RAN Sharing, and a security project on RAN and MBH.

Ivan is currently working on PhD in Technics, and owns several technical patents. 

He is a Ribbon voice in O-RAN Transport and Architecture Working Groups and a co-author of IETF drafts in Network Slicing.

 

 

ETSI TeraFlowSDN: an open and cloud-native SDN Controller for the 6G Era

Abstract: This invited talk will present the ETSI TeraFlowSDN controller, an open-source SDN controller designed to provide scalability and flexibility, some of the addressed use cases, and our future work plans.

Lluís Gifre is a Senior Researcher in the Packet-Optical Networks and Services (PONS) research unit, part of the Centre Tecnològic de Telecomunicacions de Catalunya (CTTC). He is the chair of the Technical Steering Committee (TSC) of ETSI TeraFlowSDN OSG and an IEEE Senior Member. He participated in 17 EC/nationally-funded projects and industrial collaborations. He is the lead inventor of 1 patent, and he has co-authored more than 80 publications including peer-reviewed and indexed journals, recognized international conferences, and book chapters. His research interests include cloud-based platforms in support of Software-Defined Networks (SDN) and Network Function Virtualization (NFV), network slicing and automation, and orchestration of 5G/6G infrastructures.

 

 Contributed papers

 

Design and Implementation of a Slice as a Service Architecture on the Edge Cloud with Resource Constraints

Rodrigo Ferraz Azevedo (Federal University of São Carlos, Brazil); Luciano Bernardes de Paula (IFSP, Brazil); Fábio Luciano Verdi (Federal University of São Carlos, Brazil)

Abstract: The Cloud Network Slicing (CNS) is a new concept that describes a mechanism to provide computing, networking, and storage as a virtual slice entity, enabling new approaches to IoT applications and structuring resources at the edge of the network. In this paper, the architecture defined in the NECOS Project is adopted and the functions for creating CNS in resource-constrained edge devices were designed and implemented. The implementation was evaluated on Single Board Computers (SBCs), using lightweight virtualization solutions (microservices) and the results achieved show that it is possible to instantiate CNSs on those hardware, however also show some limitations of multiple slice support on resource-constrained devices.

 

 

Auction-based network slicing for 5G RAN

Ligia Maria Moreira Zorello, Kazem Eradatmand, Sebastian Troia and Achille Pattavina (Politecnico di Milano, Italy); Yingqian Zhang (Eindhoven University of Technology, The Netherlands); Guido Maier (Politecnico di Milano, Italy)

Abstract: Network slicing is an important characteristic of 5G/6G networks that increases flexibility and enables different applications over a single infrastructure. The physical resources are partitioned to create virtualized networks, each dedicated to services with specific requirements. Several entities participate in network slicing, including Mobile Network Operators (MNOs), Mobile Virtual Network Operators (MVNOs), and users. An MNO owns the physical network infrastructure and the resources. MVNOs lease resources from the MNO and operate as service providers towards their subscribers. The goal of this work is to optimize the end-to-end network slicing process to provide services to users with a fair sharing of resources. We model this problem as a hierarchical combinatorial auction with a modified Vickrey-Clarke-Groves pricing mechanism. In the upper-level auction, an MNO is the seller supplying Network Slice to several MVNOs, who act as the bidders. In the lower-level auction, each MVNO holds an auction as a seller delivering services to their subscribed end-users, who play the role of bidders. We formulate and solve the Winner Determination Problem using mathematical programming and heuristic algorithms. The simulations show that the model can achieve fair sharing of resources, and it enables improving the MNO and MVNO revenue.

 

 

Flow classification for network security using P4-based Programmable Data Plane switches

Aniswar S Krishnan and Krishna M. Sivalingam (Indian Institute of Technology Madras, India); Gauravdeep Shami (Network Innovation Engineer & Ciena Corporation, Canada); Marc Lyonnais (Senior Advisor, Network Architecture/ External Research, Canada); Rodney Wilson (Chief Technologist, Research Networks, Canada)

Abstract: This paper deals with programmable data plane switches that perform flow classification using machine learning (ML) algorithms. This paper describes the implementation-based study of an existing ML-based packet marking scheme called FlowLens. The core algorithm, written in the P4 language, generates features, called flow markers, while processing packets. These flow markers are an efficient formulation of the packet length distribution of a particular flow. Secondly, a controller responsible for configuring the switch, extracting the features periodically, and applying machine learning algorithms for flow classification, is implemented in Python. The generation of flow markers is evaluated using flows in a tree-based topology in Mininet using the P4-enabled BMv2 packet switch on the mininet emulator. Classification is performed for the detection of two different types of network attacks: Active Wiretap and Mirai Botnet. In both cases, we obtain a 30-fold reduction in memory footprint with no loss in accuracy demonstrating the potential of running P4-based ML algorithms in packet switches.

 

 

From Automation to Autonomous: Driving the Optical Network Management to Fixed Fifth-generation (F5G) Advanced

Haomian Zheng (Huawei Technologies Co., China); Italo Busi (Huawei Technologies, Italy); Zhou Jun (Huawei Technologies, France); Yunbin Xu (China Academy of Information Communications Technology, China); Ricard Vilalta, Ramon Casellas and Raul Muñoz (Centre Tecnològic de Telecomunicacions de Catalunya (CTTC/CERCA), Spain)

Abstract: An autonomous optical network operates with little or no human involvement and has the capability to configure, oversee, and sustain itself without external intervention. This paper presents the latest advances of autonomous networking (AN) proposed in TM Forum for optical networks. Each step in the procedure for the operator's daily work is mapped into the AN framework, with detailed features specified in each level. The solution is based on a standard architecture and data models specified in IETF, known as Abstraction and Control of Traffic Engineering Networks (ACTN). Use cases are presented and conducted. This paper presents the result in three typical use cases for optical network management and maintenances: optical service provisioning, healthy assurance, and intelligent alarm processing.