Towards virtual networks for virtual machine grid computing
Bibliographic record
Abstract
Virtual machines can greatly simplify wide-area distributed computing by lowering the level of abstraction to the benefit of both resource providers and users. Networking, however, can be a challenge because remote sites are loath to provide connectivity to any machine attached to the site network by outsiders. In response, we have developed a simple and efficient layer two virtual network tool that in effect connects the virtual machine to the home network of the user, making the connectivity problem identical to that faced by the user when connecting any new machine to his own network. We describe this tool and evaluate its performance in LAN and WAN environments. Next, we describe our plans to enhance it to become an adaptive virtual network that will dynamically modify its topology and routing rules in response to the offered traffic load of the virtual machines it supports and to the load of the underlying network. We formalize the adaptation problem induced by this scheme and take initial steps to solving it. The virtual network will also be able to use underlying resource reservation mechanisms on behalf of virtual machines. Both adaptation and reservation will work with existing, unmodified applications and operating systems.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| 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".