Bibliographic record
Abstract
This thesis focuses on scalability of a resource augmentation environment when a large number of mobile devices and multiple service nodes are present. To deal with congestion, a scanning method was proposed to get information on users' density in an area such that the service nodes and access points could be placed at strategic points. To lower communication overhead, a centralized broker-node architecture was proposed, which manages resource monitoring on behalf of all mobile devices. In the centralized architecture, mathematical models for the task scheduling problem in the local resources case and the mobile cloud computing case were proposed to optimally minimize the total energy consumption across all mobile devices. A generalized model for the task scheduling problem was proposed. The model optimally minimized the total energy and monetary cost when evaluated in two environments for mobile cloud computing, one using a local private cloud and the other using public clouds. The models found optimal solutions for the centralized task scheduling problems, and an improvement in the total costs was observed when offloading with optimization compared to when offloading without optimization using the centralized task scheduler. xiii 5.5 Two Linux containers representing a service node and a mobile device connected through ns-3 WiFi network. . . . . . . . . . . . . . . . . . 5.6 Effect of number of servers in the serverApp() service on resource monitoring time. . . . . . . . . . .
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".