SLICING FUTURE INTERNET INFRASTRUCTURES
sfi2

OBJECTIVES

This research's leading technical and breakthrough emphasis is to develop a network slicing solution that provides orchestration and allocation of resources in multi-domain experimentation network infrastructures.

Machine learning techniques will assist slicing in multi-domain network infrastructures to provide near-automatic resource identification and allocation.

Research Topics

Multi-domain Slicing, Slice-as-a-Service, Experimental Testbed, ML-assisted Resource Orchestration, ML-assisted Resource Allocation, Self-driving Network, Energy Efficiency, Future Internet.

institutions

Universidade Salvador - UNIFACS, Universidade de São Paulo - USP, Universidade Federal de Uberlândia - UFU, Universidade Federal do Rio de Janeiro - UFRJ, Pontifícia Universidade Católica do Rio Grande do Sul - PUC-RS, Universidade Federal do Rio Grande do Sul - UFRGS, Universidade Federal de Pernambuco - UFPE, Universidade Federal de Minas gerais - UFMG, Universidade Federal de Goiás - UFG, Universidade Federal Fluminense - UFU, Universidade Federal da Bahia - UFBA, Instituto Tecnológica da Aeronáutica - ITA, Universidade Federal do Espírito Santo - UFES, Universidade do Vale dos Sinos - UNISINOS.

funding

Fundação de Apoio à Pesquisa do Estado de São Paulo - FAPESP

sfi2 general view

The technological evolution centered on the concepts of Software Defined Networks (SDN), Network Functions Virtualization (NFV), Cloud Computing, Internet of Things (IoT) and, more recently, on the fifth generation of mobile telecommunications networks, known as 5G, has caused great impact on the Internet. This impact comes not only from the creation of new technologies, communication protocols, security solutions, energy efficiency mechanisms and others, but also from the need to jointly operate this plethora of innovations with each other and with the legacy of the Internet. As a result, several experimentation environments with different scopes have emerged in Brazil, targeting at the research of different subsets of technologies, among them: FIBRE, experimentation of alternative architectures to the current Internet; FUTEBOL, experimentation in telecommunications networks involving optical and wireless communications; CloudNEXT, experimentation on cloud computing and bare-metal provisioning; FIWARE, future Internet-Based applications based on Internet of Things (IoT), Big Data and Cloud Computing; 5GINFIRE, experimentation in 5G networks, based on NFV and cloud usage; and finally NECOS, focused on the creation of slices that encompass different clouds with distributed resources. In this scenario, the main objective of this project is to provide a solution for the provision of advanced network scenarios and allocation of computational resources by slicing these multi-domain experimentation infrastructures, in the simplified creation of complex networks with minimum configuration effort based on the intelligent orchestration of this multi-domain slicing, offering Slice-as-a Service (SlaaS) for future Internet developers.

activities

WP 1 - Project Management

The main objective of this work package is to ensure efficient project management and to monitor the progress of the activities described in its proposal.

Partner institutions: USP

WP 2 - SFI2 Architecture

This work package's main objective is to define an architecture for SFI2 encompassing the different technologies present in the testbeds that support the project and a set of use cases for experimentation and validation of SFI2.

Partner institutions: UFG, UFU, UFPA, UFRGS and UFES

WP 3 - Intelligent Infrastructure for Experimentation

This work package's main objective is to provide intelligent mechanisms for provisioning and maintaining slices in a multi-domain environment with the support of machine learning techniques. Multi-domain network slicing providing slice as a service (SlaaS) is the main target.

Partner institutions: ITA, UNIFACS, UFU, UFMG, UFRJ and PUC-RS

WP 4 - Monitoring and Energy Efficiency

The main objective of this working package is to monitor the SFI2 infrastructure and orchestrate energy efficiency mechanisms applied to the resources of this environment based on multi-domain slicing.

Partner institutions: UFPE, USP, UFMG, UFRGS, UNISINOS and UFF

WP 5 - Security

This WP aims to bring functional aspects essential for good performance of the infrastructure and ensure the integrity and reproducibility of the experiments from the point of view of safety and slice isolation.

Partner institutions: UFBA, UFES, ITA e UFU

WP 6 - Dissemination, Data Management and Standardization

This WP will be responsible for using different communication strategies to disseminate results, increase SFI2 visibility and adoption, exploit its results, manage the data generated, and timely propose topics of interest to standardization bodies.

Partner institutions: USP, UFRJ and RNP

deliverables

https://sites.google.com/view/sfi2/home/sfi2-deliverables

team UNIFACS

Prof. Dr. Joberto S. B. Martins (UNIFACS) - Principal Investigator (PI)

Professor at Salvador University (UNIFACS) and PhD in Computer Science at Université Pierre et Marie Curie - UPMC, Paris (1986). International Professor at HTW - Hochschule für Techknik und Wirtschaft des Saarlandes (Germany) since 2004, Senior Research Period at Université of Paris-Saclay in 2016, Salvador University head and researcher at NUPERC and IPQoS research groups on Resource Allocation Models, Machine Learning, Software Defined Networking - OpenFlow, Smart Cities, Smart Grid and Cognitive Management. Previously worked as Invited Professor at Université Paris VI and Institut National des Télécommunications (INT) in France and as key speaker, teacher and invited lecturer in various congresses and institutions in Brazil and Europe..

Eduardo Sidney Xavier (UNIFACS)

M. Sc. student at Universidade Salvador (UNIFACS), gratuated student in e-learning at Serviço Nacional de Aprendizagem Comercial (SENAC) and specialist in data processing at Universidade Federal do Pará (UFPA). Intersted in database systems, distributes systems, machine learning and robotics.

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