Applying Machine Learning to End-to-end Slice SLA Decomposition

Iannelli, Michael; Rahman, Muntasir Raihan; Choi, Nakjung; Wang, Le

doi:10.1109/netsoft48620.2020.9165317

Cited by 13 publications

(6 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It intelligently understands the network environment and determines the necessary action in dynamic situations by learning from the historical QoS anomalies and extracting the required information to approximate the correlations autonomously between the historical data and actual QoS anomalies. Moreover, Lannelli et al [165] proposed a design and implementation of SLA decomposition for end-to-end slice instances consisting of multiple operator domains using the EL approach with RF, GB, and ANN-based base classifiers. The GB with ANN performance was better than the RF with ANN.…”

Section: Smart Sla Assurancementioning

confidence: 99%

A Survey on Requirements of Future Intelligent Networks: Solutions and Future Research Directions

2022

View full text Add to dashboard Cite

The context of this study examines the requirements of Future Intelligent Networks (FIN), solutions, and current research directions through a survey technique. The background of this study is hinged on the applications of Machine Learning (ML) in the networking field. Through careful analysis of literature and real-world reports, we noted that ML has significantly expedited decision-making processes, enhanced intelligent automation, and helped resolve complex problems economically in different fields of life. Various researchers have also envisioned future networks incorporating intelligent functions and operations with the ML. Several efforts have been made to automate individual functions and operations in the networking domain; however, most of the existing ML models proposed in the literature lack several vital requirements. Hence, this study aims to present a comprehensive summary of the requirements of FIN and propose a taxonomy of different network functionalities that needs to be equipped with ML techniques. The core objectives of this study are to provide a taxonomy of requirements envisioned for end-to-end FIN, relevant ML techniques, and their analysis to find research gaps, open issues, and future research directions. The real benefit of machine learning applications in any domain can only be ensured if intelligent capabilities cover all its components. We observed that future generations of networks are heterogeneous, multi-vendor, and multidimensional, and ML can provide optimal results only if intelligent capabilities are used on a holistic scale. Realizing intelligence on a holistic scale is only possible if the ML algorithms can solve heterogeneous problems in a multi-vendor and multidimensional environment. ML models must be reliable and efficient, support distributed learning architecture, and possess the capability to learn and share the knowledge across the network layers and administrative domains to solve issues. Firstly, this study ascertains the requirements of the FIN and proposes their taxonomy through reviews on envisioned ideas by various researchers and articles gathered from reputed conferences and standard developing organizations using keyword queries. Secondly, we have reviewed existing studies on ML applications focusing on coverage, heterogeneity, distributed architecture, and cross-domain knowledge learning and sharing. Our study observed that in the past, ML applications were focused mainly on an individual/isolated level only, and aspects of global and deep holistic learning with cross-layer/domain knowledge sharing with agile ML operations are not explored at large. We recommend that the issues mentioned above be addressed with improved ML architecture and agile operations and propose ML pipeline-based architecture for FIN. The significant contribution of this study is the impetus for researchers to seek ML models suitable for a modular, distributed, multi-domain and multi-layer environment and provide decision-making on a global or holistic rather than individual function level.

show abstract

Section: Smart Sla Assurancementioning

confidence: 99%

A Survey on Requirements of Future Intelligent Networks: Solutions and Future Research Directions

2022

View full text Add to dashboard Cite

show abstract

“…However, although several proposals point to promising paths, it is not yet possible to aggregate the various features in a unique and fully functional approach, which defines the operation and management mechanisms of each slice, in addition to providing subsidies for scalability, orchestration and support decision-making, in domains involving heterogeneous technologies and access methods (e.g., 5G, LTE, Wi-Fi, Wireline) [57,58].…”

Section: G-related Workmentioning

confidence: 99%

A Multi-Provider End-to-End Dynamic Orchestration Architecture Approach for 5G and Future Communication Systems

et al. 2021

View full text Add to dashboard Cite

Network segregation is the solution adopted in the IMT-2020 standardization of the International Telecommunications Union (ITU), better known as 5G networks (Fifth Generation Mobile Networks), under development to meet the requirements of performance, reliability, energy, and economic efficiency required by applications in the various verticals of current and near-future economic activities. The philosophy adopted for the IMT-2020 standardization relies on the use of Software-Defined Networking (SDN), Network Function Virtualization (NFV), and Software-Defined Radio (SDR), i.e., the softwarization of the network. Softwarization allows network segregation through its slicing, which is discussed herein this work. Network slicing is performed by a novel Orchestrator, as provided in IMT-2020, which maintains the end-to-end network slices independent of each other and performs horizontal handover when the possibility of a loss of Quality of Service (QoS) is predictively detected by monitoring quality parameters during operation. Therefore, the Orchestrator is dynamic, operates in uptime, and allows horizontal handover. Hence, it chooses the most appropriate telecommunication infrastructure provider and network operator to guarantee QoS and Quality of Experience (QoE) to end-users in each network segment. These features make this work modern and keep it aligned with the actions being carried out by ITU. Based on this objective, as the main result of this paper, we propose an effective architecture for implementing the Orchestrator, not only to contribute to the state of the art for 5G and beyond communication systems but also to generate economic, technological, and social impacts.

show abstract

“…The method has simple operations and calculations; however, the results are stable and it considers the relationships between gains and losses in its judgments, which allows for a 'reasonable' decision-making process. These characteristics allow its use in conjunction with other methods, resulting in more refined solutions (WANG et al, 2020).…”

Section: Multicriteria Methodsmentioning

confidence: 99%

“…Evidence of this can be found in the solutions that do or do not consider the process of inter-network mobility. SHI et al, 2021;WANG et al, 2020).…”

Section: Slices Classification Techniquesmentioning

confidence: 99%

“…The default configuration was utilized for the remaining methods. A (NGUYEN et al, 2022a;WANG et al, 2020;ULUTAş et al, 2020;DEZERT et al, 2020;KUNDAKCı, 2019;YAZDANI et al, 2019;ALINEZHAD;KHALILI, 2019;WA ¸TRÓBSKI et al, 2019;HEZER;GELMEZ;ÖZCEYLAN, 2021;BAK-MAZ;BAKMAZ, 2019;BABASHAMSI et al, 2016;FIGUEIRA;GRECO;EHRGOTT, 2005)).…”

Section: Nssf Daf: Network Slice Selection Strategymentioning

confidence: 99%

See 1 more Smart Citation

Uma abordagem para o serviço de seleção de slices para ambientes multidomínio em redes móveis 5G e sistemas de comunicação futuros.

Silva

View full text Add to dashboard Cite

Uma abordagem para o serviço de Seleção de Slices para Ambientes Multidomínio em Redes Móveis 5G e Sistemas de Comunicação Futuros O serviço Network Slice Selection Function (NSSF) em ambientes de tecnologia heterogêneos é um problema complexo, que ainda não tem uma solução totalmente aceitável. Assim, a implementação de novas estratégias de seleção de slices representa uma questão importante em desenvolvimento, principalmente devido à crescente demanda em aplicações e cenários envolvendo redes 5G e futuras. Este trabalho apresenta uma solução integrada para o problema NSSF, denominado Network Slice Selection Function Decision-Aid Framework (NSSF DAF), que consiste em uma solução distribuída, onde uma parte é executada no equipamento do usuário (e.g. smartphones, VANTs, Brokers IoT), funcionando como um serviço transparente e outra à borda da operadora ou do provedor de serviços. Para tanto, protocolos e ferramentas de software são usados para classificar os slices. Neste trabalho, 14 métodos multicritérios são empregados para auxiliar na tomada de decisão, sendo eles: ARAS, COCOSO, CODAS, COPRAS, EDAS, MABAC, MAIRCA, MARCOS, MOORA, OCRA, PROMETHEE II, SPOTIS, TOPSIS e VIKOR. O objetivo geral consiste em verificar a semelhança entre esses métodos e aplicações no processo de classificação e seleção de slices, considerando um cenário específico. Para realizar a seleção é utilizado aprendizado de máquina por meio do algoritmo de agrupamento K-means, adotando uma solução híbrida para a implementação e operação do serviço NSSF em ambientes de slices multi-domínio em redes móveis heterogêneas. Testes de bancada foram conduzidos visando validar a abordagem proposta, mapeando e correlacionando os requisitos dos serviços com os slices disponíveis. Os resultados indicam uma possibilidade real de oferecer uma solução completa para o problema NSSF que pode ser implementada na borda ou no núcleo da rede, ou mesmo na própria Estação Rádio Base 5G, sem custo computacional incremental ao equipamento do usuário final, garantindo a qualidade de experiência adequada ao consumo dos seus serviços.

show abstract

Applying Machine Learning to End-to-end Slice SLA Decomposition

Cited by 13 publications

References 9 publications

A Survey on Requirements of Future Intelligent Networks: Solutions and Future Research Directions

A Survey on Requirements of Future Intelligent Networks: Solutions and Future Research Directions

A Multi-Provider End-to-End Dynamic Orchestration Architecture Approach for 5G and Future Communication Systems

Uma abordagem para o serviço de seleção de slices para ambientes multidomínio em redes móveis 5G e sistemas de comunicação futuros.

Contact Info

Product

Resources

About