Evidence map›Paper›PMID 37447902›Full record

ArticleSensors (Basel, Switzerland)2023

Efficient Network Slicing with SDN and Heuristic Algorithm for Low Latency Services in 5G/B5G Networks.

Robert Botez, Andres-Gabriel Pasca, Alin-Tudor Sferle, Iustin-Alexandru Ivanciu, Virgil Dobrota

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Robert BotezCommunications Department, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania.ORCID 0000-0001-7173-4405
Andres-Gabriel PascaCommunications Department, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania.
Alin-Tudor SferleCommunications Department, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania.
Iustin-Alexandru IvanciuCommunications Department, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania.ORCID 0000-0002-5648-8442
Virgil DobrotaCommunications Department, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania.ORCID 0000-0001-5917-4003

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

5Gcloud computingKubernetesnetwork slicingNFVSDN

Identifiers

PMID37447902
PMCPMC10346795

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.