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.
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.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.006 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".