A Residual LSTM based Multi-Label Classification Framework for Proactive SLA Management in a Latency Critical NFV Application Use-Case

Jalodia, Nikita; Taneja, Mohit; Davy, Alan; Dezfouli, Behnam

doi:10.1109/ccnc49033.2022.9700502

Cited by 3 publications

(1 citation statement)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, there has recently been a shift towards strict network-level reliability that takes into account user data traffic demand, particularly for nextgeneration cellular networks. For example, 5G networks are expected to provide 99.999% (or "five nines") of data availability annually [11], with plans to improve to a seven-nines standard in 6G [12]. Given the nature of these requirements, the characterization of the expected demand not satisfied metric in such systems becomes especially important [13].…”

Section: Introductionmentioning

confidence: 99%

A Framework for the Evaluation of Network Reliability Under Periodic Demand

Maatouk¹,

Ayed²,

Shi³

et al. 2023

Preprint

View full text Add to dashboard Cite

In this paper, we study network reliability in relation to a periodic time-dependent utility function that reflects the system's functional performance. When an anomaly occurs, the system incurs a loss of utility that depends on the anomaly's timing and duration. We analyze the long-term average utility loss by considering exponential anomalies' inter-arrival times and general distributions of maintenance duration. We show that the expected utility loss converges in probability to a simple form. We then extend our convergence results to more general distributions of anomalies' inter-arrival times and to particular families of nonperiodic utility functions. To validate our results, we use data gathered from a cellular network consisting of 660 base stations and serving over 20k users. We demonstrate the quasi-periodic nature of users' traffic and the exponential distribution of the anomalies' inter-arrival times, allowing us to apply our results and provide reliability scores for the network. We also discuss the convergence speed of the long-term average utility loss, the interplay between the different network's parameters, and the impact of non-stationarity on our convergence results

show abstract

Section: Introductionmentioning

confidence: 99%

A Framework for the Evaluation of Network Reliability Under Periodic Demand

Maatouk¹,

Ayed²,

Shi³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

An Optimization Framework for Anomaly Detection Scores Refinement with Side Information

Maatouk,

Ayed,

et al. 2023

GLOBECOM 2023 - 2023 IEEE Global Communications Conference

View full text Add to dashboard Cite

A Survey of Intelligent End-to-End Networking Solutions: Integrating Graph Neural Networks and Deep Reinforcement Learning Approaches

Tam,

Ros,

Song

et al. 2024

Electronics

View full text Add to dashboard Cite

This paper provides a comprehensive survey of the integration of graph neural networks (GNN) and deep reinforcement learning (DRL) in end-to-end (E2E) networking solutions. We delve into the fundamentals of GNN, its variants, and the state-of-the-art applications in communication networking, which reveal the potential to revolutionize access, transport, and core network management policies. This paper further explores DRL capabilities, its variants, and the trending applications in E2E networking, particularly in enhancing dynamic network (re)configurations and resource management. By fusing GNN with DRL, we spotlight novel approaches, ranging from radio access networks to core management and orchestration, across E2E network layers. Deployment scenarios in smart transportation, smart factory, and smart grids demonstrate the practical implications of our survey topic. Lastly, we point out potential challenges and future research directions, including the critical aspects for modelling explainability, the reduction in overhead consumption, interoperability with existing schemes, and the importance of reproducibility. Our survey aims to serve as a roadmap for future developments in E2E networking, guiding through the current landscape, challenges, and prospective breakthroughs in the algorithm modelling toward network automation using GNN and DRL.

show abstract

A Residual LSTM based Multi-Label Classification Framework for Proactive SLA Management in a Latency Critical NFV Application Use-Case

Cited by 3 publications

References 26 publications

A Framework for the Evaluation of Network Reliability Under Periodic Demand

A Framework for the Evaluation of Network Reliability Under Periodic Demand

An Optimization Framework for Anomaly Detection Scores Refinement with Side Information

A Survey of Intelligent End-to-End Networking Solutions: Integrating Graph Neural Networks and Deep Reinforcement Learning Approaches

Contact Info

Product

Resources

About