OpenDaylight and OpenNebula integration: Testing traffic management
Bibliographic record
Abstract
Software-Defined Networking (SDN) and Cloud Computing are now two of the most adopted technologies, on which many organizations are working to enhance every day. For instance, SDN is particularly emerging to solve networking complexity in cloud data centers, so we see many attempts to integrate Network and Cloud Managers. In this paper, we address an integration of these two technologies, particularly a yet undiscussed combination of two popular frameworks: OpenNebula and OpenDaylight. These open source solutions are widely used for cloud management and network management, yet there are no developed modules for communication between the two. Therefore, we propose a simple way for OpenDaylight to manage OpenNebula's compute nodes, using a common component they both support: OpenvSwitch. We compared OpenNebula with the popular OpenStack cloud manager, as it is attracting more attention in both academia and industry, by evaluating some relevant time metrics and discussing the differences of the proposed technologies. Then, we deployed a test topology to conduct some traffic management techniques in this integration. Our results show that OpenNebula's deployment time as well as clean-up time is significantly lower than OpenStack, but OpenStack takes less time to the running state, besides proving the simplicity of traffic management in OpenNebula using OpenDaylight.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".