Joint Multi-User Computation Offloading and Data Caching for Hybrid Mobile Cloud/Edge Computing
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, we investigate a hybrid mobile cloud/edge computing system with coexistence of centralized cloud and mobile edge computing, which enables computation offloading and data caching to improve the performance of users. Computation offloading and data caching decisions are jointly optimized to minimize the total execution delay at the mobile user side, while satisfying the constrains in terms of the maximum tolerable energy consumption of each user, the computation capability of each MEC server, and the cache capacity of each access point (AP). The formulated problem is non-convex and challenging because of the highly coupled decision variables. To address such an untractable problem, we first transform the original problem into an equivalent convex one by McCormick envelopes and introducing auxiliary variables. To the end, we propose a distributed algorithm based on the alternating direction method of multipliers (ADMM), which can achieve near optimal computation offloading and data caching decisions. The proposed algorithm has lower computational complexity compared to the centralized algorithm. Simulation results are presented to verify that the proposed algorithm can effectively reduce computing delay for end users while ensuring the performance of each user.
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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 | 0.001 | 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 | 0.001 |
| 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 it