Network Slicing Resource Allocation ML-Enhanced (NSRALLOC-ML)

OBJECTIVES

The general objective of the NSRAlloc-ML project is to develop a new set of solutions for network slicing aided by machine learning with dynamic allocation of communication resources (bandwidth).

Specific Objectives:

Investigate strategies, requirements, dependencies, and a functional architecture for network slicing aided by machine learning techniques.
Model and develop modules using machine learning to support network slicing with the orchestration and allocation of communication (bandwidth) resources using Q-Learning, SARSA, Deep Learning, and CBR machine learning techniques.
Evaluating the efficiency and scalability of machine learning techniques Q-Learning, SARSA, and Deep Learning for bandwidth allocation in network slicing.
Creation of simulation DATASET referring to network slicing with an intelligent allocation of communication resources with Q-Learning, SARSA, and Deep Learning for an IP/MPLS network.
Prototyping the DyRA framework (Framework for Dynamic Resource Allocation) for dynamic and intelligent allocation of communication resources in the context of network slicing.

Research Topics

Network Slicing, Resource Orchestration and Allocation, Machine Learning, Software-defined Networking, OpenFlow, Publish/ Subscribe.

institutions

Universidade Salvador - UNIFACS
Universidad Andres Bello - UAB (Chile)
University of Petroleum and Energy Systems - UPES (India)
Instituto Federal de Educação da Bahia - IFBA - Valença
Universidade Federal da Bahia - UFBA

funding

Salvador University - UNIFACS
Instituto ANIMA
Coordenação da Aperfeiçoamento do Pessoal do Ensino Superior - CAPES
Conselho Nacional de Desenvolvimento Científico e Tecnológico - CNPQ
Fundação de Apoio à Pesquisa do Estado da Bahia - FAPESB

nsralloc-ml general view

Network slicing is a technology that has already been used in the context of 5G networks. In this context, the concept of slicing a network as a service (Slice-as-a-Service - SlaaS) is used by Mobile Network Operators (MNO) to create customized networks creating a new business model. Network slicing is expanding its use in other scenarios of telecommunication networks, content provider networks (ISPs), experimental networks, and IoT systems, among others. Network slicing is an enabler that virtualizes the various resources of the network with big gains in cost, efficiency, and scale for companies and institutions.

The NSRAlloc-ML (Network Slicing Resource Allocation ML-Enhanced) project proposes the modeling and development of network slicing strategies with a communication resource allocation capable of supporting the dynamism and elasticity of the end-to-end communication of the slices. NSRAlloc-ML uses new resource allocation models with bandwidth allocation models (BAM) aided by Q-Learning, SARSA, Deep Learning, and CBR (Case-Based Reasoning) algorithms for resource slicing and bandwidth allocation in networks.

nsralloc-ml activities

ACTIVITY 1: Investigation of Strategies, Requirements and Functional Architecture for Network Slicing and Communication Resource Allocation

This activity aims to identify the basic network slicing strategies, their requirements, and dependencies concerning the communication resource allocation functionality for the orchestration, implementation, and management. Slice elasticity and network admission control concerning communication resources are focal points of this activity.

ACTIVITY 2: Machine Learning for Intelligent Allocation of Communication Resources in Network Slicing

This activity aims at the development of machine learning-based knowledge acquisition modules for the DyRA framework. The activity includes modeling, developing algorithms, and simulating the learning associated with the strategy and mechanism of orchestration and allocation of network slice communication resources.

ACTIVITY 3: Development of the DyRA Framework for Slicing with Smart Communication Resource Allocation

This activity aims to implement the DyRA framework, embed the modules and function blocks necessary to deploy use cases.

ACTIVITY 4: Use Cases

This activity aims at prototyping network slicing cases in specific application contexts. The activity aims to apply and validate the cognition modules with the functional requirements of the application scenario.

Smart cities and smart grid will be the main focus.

ACTIVITY 5: Dissemination

This permanent activity corresponds to the dissemination of the results of the NSRAlloc-ML Project through workshops, lectures, and institutional articulations.

deliverables

UNDER DEVELOPMENT

team

Prof. Dr. Joberto S. B. Martins (UNIFACS) - Project Coordinator

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 a key speaker, teacher, and invited lecturer in various congresses and institutions in Brazil and Europe..

Prof. Dr. Eldman Nunes - Collaborator

Professor at Salvador University (UNIFACS) and PhD and Master in Computer Science at Fluminense Federal University (UFF, Brazil). Post-doctoral research in image processing at Computer Science Department of Rey Juan Carlos University (URJC, Spain). Her research interests include Computer Vision, Image Processing and Computer Intelligence.

Prof. Dr. Rafael F. Reale - Collaborator

Professor at Institute Federal of Bahia (IFBA). PhD. in Computer Science with DMCC (UFBA/UNIFACS/UEFS), MSc. in Computer Systems by Salvador University - UNIFACS (2011) and bachelor in Informatics by Universidade Católica do Salvador (2005). Professor at Instituto Federal da Bahia (IFBA). His current research interests include Bandwidth Allocation Model, MPLS, DS-TE, Autonomy, QoS, Future Internet Architectures, Software-Defined Networks and Cognitive Networks.

Eduardo Sidnei da Silva Xavier

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.

MSc. Pedro Francesco

MSc. in Computer Science at PPGCOMP/ Salvador University - UNIFACS and bachelor in Informatics by UNIFACS. Has interest in computer networks topics like Publish/ Subscribe, Network Management, Bandwidth Allocation Model and Information-Centric Networks..

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