An Empirical Evaluation of Automated Configuration Tools for Software-Defined Networking: A Usability and Performance Perspective
Bibliographic record
Abstract
The advent of Software Defined Networking (SDN) has ushered in an era where the functions of interconnected devices are no longer constrained by their original design.Instead, these devices, now transformed into "general-purpose" nodes within the network, have roles that are defined by their configuration settings.Given that these configurations can be compiled into a computer file, software tools have been developed to consolidate and automate the administration of configuration parameters across all devices in an SDN network.These tools, akin to source code control tools used in programming languages, are capable of managing configurations for individual or groups of devices simultaneously.This study presents an evaluation of three such tools-Ansible, Puppet, and Chefassessing their merits and demerits across various performance and usability dimensions, including configuration, installation, ease of use, and management capabilities.The comparative analysis reveals Ansible as a remarkably versatile tool, offering a wealth of advantages that make it a compelling choice for a majority of automation and configuration management tasks.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.043 | 0.212 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".