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Record W4387576696 · doi:10.18280/rces.100302

Comparative Analysis of SDN Controllers: A Study on Installation, Protocols Interaction, Network Topologies Monitoring, and GUI Experience

2023· article· en· W4387576696 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueReview of Computer Engineering Studies · 2023
Typearticle
Languageen
FieldComputer Science
TopicSoftware-Defined Networks and 5G
Canadian institutionsnot available
Fundersnot available
KeywordsNetwork topologyComputer scienceComputer networkProtocol (science)Distributed computingEmbedded systemHuman–computer interactionMedicine

Abstract

fetched live from OpenAlex

This paper analyses four SDN controllers that support this architecture not only from a technical point of view but also from an academic point of view by including it in the university curriculum.The integration of network controller analysis into an academic curriculum can provide a comprehensive training in theoretical and practical aspects related to network management and SDN technologies.The controllers analyzed were FloodLight, HP SDN VAN Controller, ONOS (Open Network Operating System) and AGILE SDN.Their comparison was based on criteria such as ease of installation, interaction with other communication protocols, ability to monitor network topologies and experience in using their graphical user interfaces.ONOS was found to be the most secure, reliable, robust and scalable controller.Notwithstanding the above, it is important to note that the network technology landscape is constantly evolving, so it is essential to keep updating drivers and comparing features, performance, etc. on these platforms before making a decision.The following are the factors that make ONOS the best choice: 1. Flexibility and customization: ONOS is known for being highly flexible and customizable.This means that you can adapt and customize its functionality to meet the specific needs of your network.Extensions and custom applications can be implemented more easily in ONOS than in some other controllers.2. Scalability: ONOS is designed to be scalable and can handle large networks with a large number of devices and flows.This makes it suitable for applications in service provider and enterprise network environments.3. multitechnology support: ONOS is known for its ability to manage a variety of network technologies, including OpenFlow and others.This makes it versatile in terms of support for different network equipment and technologies.4.Active community and continuous development: ONOS has an active community of developers and continuous development.This means that updates and new features are more likely to be found on a regular basis.Among the criteria used, ease of installation was chosen, allowing the controller to be deployed quickly and efficiently, which is beneficial in terms of time and cost.On the other hand, the ability to monitor network topologies provides visibility and control, which is essential for network performance, efficiency and security.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.605
Threshold uncertainty score0.625

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.085
GPT teacher head0.398
Teacher spread0.313 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it