Running applications efficiently in online social networks
Bibliographic record
Abstract
In the past several years, online social networks (OSNs) such as Facebook and MySpace have become extremely popular with Internet users. Such sites are popular with users because they simplify both communication among "communities" and access to applications. Application developers are attracted to these sites also, as they are able to exploit "word-of-mouth" marketing, which these OSN sites have embodied into their user experience. A challenge for developers though is managing the application, as it is difficult to predict how successful the marketing will be. Our solution combines an OSN, Virtual Appliances, and a utility computing environment together. We demonstrate our solution using the Facebook portal (OSN), the Fire Dynamics Simulator (application), and a utility environment we built using tools such as Condor, Moab and Xen. The application is supported using Virtual Appliances, which interact with our flexible infrastructure to dynamically expand and contract based on user demand. Thus, we are able to make much more efficient use of the underlying physical infrastructure. We believe that our solution also has great potential for enterprise IT environments. Initial feedback suggests combining an OSN with our flexible infrastructure provides a much better user experience than the traditional, standalone use of the (legacy) application, and simplifies the management and increases the effective utilization of the underlying IT resources.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".