Efficient Network Slicing with SDN and Heuristic Algorithm for Low Latency Services in 5G/B5G Networks
TL;DR: In this article , a modified A* algorithm that incorporates network quality of service parameters into a composite metric is proposed for network slicing in 5G backhaul networks, targeting services with low or very low latency requirements.
read more
Abstract: This paper presents a novel approach for network slicing in 5G backhaul networks, targeting services with low or very low latency requirements. We propose a modified A* algorithm that incorporates network quality of service parameters into a composite metric. The algorithm’s efficiency outperforms that of Dijkstra’s algorithm using a precalculated heuristic function and a real-time monitoring strategy for congestion management. We integrate the algorithm into an SDN module called a path computation element, which computes the optimal path for the network slices. Experimental results show that the proposed algorithm significantly reduces processing time compared to Dijkstra’s algorithm, particularly in complex topologies, with an order of magnitude improvement. The algorithm successfully adjusts paths in real-time to meet low latency requirements, preventing packet delay from exceeding the established threshold. The end-to-end measurements using the Speedtest client validate the algorithm’s performance in differentiating traffic with and without delay requirements. These results demonstrate the efficacy of our approach in achieving ultra-reliable low-latency communication (URLLC) in 5G backhaul networks.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
5G and Beyond 5G Technologies Enabling Industry 5.0: Network Applications for Robotics
Christina C. Lessi,Andreas Gavrielides,Vittorio Solina,Renxi Qiu,Letizia Nicoletti,Daiyou Li +5 more
TL;DR: This paper explores 5G and beyond technologies enabling Industry 5.0 through AI-powered robots, requiring innovative network applications to manage diverse, continuous, and evolving data transmission needs, focusing on application-side traffic shaping within non-public networks for optimized Quality of Experience.
5
Detection of DDoS attacks in SDN with Siberian Tiger Optimization algorithm and deep learning
Naseer Hameed Saadoon Al-Sarray,Javad Rahebi,Ayşe Demi̇rhan +2 more
- 19 Mar 2024
TL;DR: DDoS attacks targeting SDN controllers can be effectively detected using a deep learning-based approach incorporating image encryption and the Siberian tiger optimization algorithm.
2
Scheduling of Industrial Control Traffic for Dynamic RAN Slicing with Distributed Massive MIMO
Emma Fitzgerald,Michal Pióro +1 more
TL;DR: This paper proposes mixed-integer programming optimization formulations for dynamic RAN slicing in massive MIMO systems, minimizing radio resources occupied by industrial control traffic while meeting stringent latency and reliability requirements.
Coati Optimized Hybrid Neural Network for Efficient Network Slicing in 5 Generation Network
TL;DR: This paper proposes ONE-CLOUD, a hybrid neural network integrating Coati Optimization Algorithm, GhostNet, and Gated Dilated CNN for efficient network slicing in 5G networks, achieving 5.78-4.70% higher accuracy than existing DQN-E2E methods.
Weight multimedia analysis (WMA) approach for network slicing in 5G
Rasool Altaee,Chad V. Pecot +1 more
References
SDN/NFV, Machine Learning, and Big Data Driven Network Slicing for 5G
Luong-Vy Le,Bao-Shuh Paul Lin,Li-Ping Tung,Do Sinh +3 more
- 09 Jul 2018
TL;DR: This study aims to integrate various machine learning (ML) algorithms, big data, SDN, and NFV to build a comprehensive architecture and an experimental framework for the future SONs and network slicing and successfully implemented an early state traffic classification and network slices for mobile broadband traffic applications.
Guest Editorial: Intelligent Ultra-Reliable and Low-Latency Communications in 6G
TL;DR: In this article , the end-to-end (E2E) delay cannot exceed 1 ms and the packet loss probability should be 10 −5 ~ 10 −7.
A Fuzzy Logic-Based Intelligent Multiattribute Routing Scheme for Two-Layered SDVNs
TL;DR: In this paper , an intelligent multi-attribute routing scheme (MARS) for two-layered software-defined vehicle networks (SDVNs) is proposed by employing fuzzy logic and design a technique of order preference by similarity to ideal solution (TOPSIS) algorithm to find the next hop forwarder.
Panorama: Real-time bird's eye view of an OpenFlow network
Ankit Gangwal,Mauro Conti,Manoj Singh Gaur +2 more
- 01 May 2017
TL;DR: This paper presents a collection of lightweight mechanisms for obtaining real-time network information in SDN environment, which aim to obtain per-flow and per-port traffic statistics, topology information, data transfer rate for each network link, etc.