Stochastic modeling and analysis of opportunistic computing in intermittent mobile cloud
Bibliographic record
Abstract
We have witnessed a rapid evolvement in the embedded mobile devices, such as mobile phones and vehicles, with various processing capabilities, some of which are even beyond the common computers. They constitute an unstructured distributed environment with huge computation potential. On the other hand, intermittently connected networks provide a way for delay-tolerant communication between these devices. Such evolution enables a new opportunistic delay-tolerant mobile computing paradigm, called Intermittent Mobile Cloud (IMC), where all transmissions are made over an intermittently connected network and the computation tasks are off oaded to certain service providers that voluntarily share their computational resources in the cloud. In this paper, we first study the capacity of epidemic routing using Random Linear Network Coding (RLNC), which is a critical and fundamental communication issue in IMC. The results are then applied to provide further insights towards a design of efficient bandwidth allocation scheme to minimize the expected completion time of a suite of parallel tasks for a service requester in IMC.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".