Dynamic publishing and availability management of virtual machines in Virtual Organization
Bibliographic record
Abstract
Nowadays, universities and research institutes are more and more feeling the need to establish collaboration networks for sharing skills, as well as material, human, software and virtualized resources. This refers to the idea of Virtual Organizations (VOs) which allow any user of a member organization to access technological and pedagogical resources available in another partner organization. However, this collaboration, most of the time, raises some concern for member organizations. Indeed, they are afraid not to be able to use their own resources when they need to. Information on resources availability is generally static, provided at a given moment. The presence management of virtual machines with the XMPP (Extensible Messaging and Presence Protocol) protocol presents limitations in the context of a collaborative network. So, the constraints identified in terms of resource sharing, instant availability and restrictions are the main obstacles to setting up such a virtual organization. This paper aims to propose and implement an application that manages dynamic discovery and availability of shared virtual machines. The proposed system manages in real time the presence of a resource and its availability in terms of its occupation or use. The system will also allow a member organization to make its resources available and enjoy priority on its resources in case it needs to use them.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".