Cloud4Home -- Enhancing Data Services with @Home Clouds
Bibliographic record
Abstract
Mobile devices, net books and laptops, and powerful home PCs are creating ever-growing computational capacity at the periphery of the Internet, and this capacity is supporting an increasingly rich set of services, including media-rich entertainment and social networks, gaming, home security applications, flexible data access and storage, and others. Such 'at the edge' capacity raises the question, however, about how to combine it with the capabilities present in the cloud computing infrastructures residing in data center systems and reachable via the Internet. The Cloud4Home project and approach presented in this paper addresses this topic, by enabling and exploring the aggregate use of @home and @datacenter computational and storage capabilities. Cloud4Home uses virtualization technologies to create content storage, access, and sharing services that are fungible both in terms of where stored objects are located and in terms of where they are manipulated. In this fashion, data services can provide low latency response to @home events as well as high throughput response when the higher and less predictable latencies of data center access can be tolerated. Cloud4Home is implemented with the Xen open source hypervisors for standard x86-based mobile to server platforms, and is evaluated using sample applications based on home security and video conversion services.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".