Towards efficient data access in mobile cloud computing using pre-fetching and caching
Bibliographic record
Abstract
Mobile devices nowadays can connect to the network very conveniently using cellular data network or WiFi. However, latency is still a challenge caused by the stability and the availability of the network, mainly in the context of mobile environments. In this paper, we propose an architecture based on a Cloudlet model using CAching and pre-FEtching scheme (CAFE scheme) to improve data access efficiency. The prefetching scheme on the Cloud enables the retrieval of specific data in advance based on specific information of users. On the Cloudlet, a caching technique selectively stores data passing through the Cloudlet. The classification of data into specific and general takes both individual access behavior and common trends into consideration. Compared to an original model, the experiment results show that our architecture can really decrease latency and improve data access efficiency when users request data from a Cloudlet and the Cloud.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".