Mixed-Timescale Joint Computational Offloading and Wireless Resource Allocation Strategy in Energy Harvesting Multi-MEC Server Systems
Bibliographic record
Abstract
As an emerging paradigm enabling mobile devices to leverage additional computation resources from nearby MEC servers (MSs), mobile edge computing (MEC) has drawn great attention from academia to industry. Unlike the conventional cloud server, the MEC provides a medium-scale and portable computation ability at MSs without relying on the time-consuming and capacity-constrained backhaul. However, the MEC offloading process is still highly sensitive to the fluctuation of both radio and computing resources. In this paper, considering the independent variation of the wireless channel conditions and computing tasks, we propose a Mixed-timescale Joint Computational offloading and Wireless resource allocation (MJCW) algorithm for latency-critical applications, aiming at minimizing the total energy consumption. Through such a new approach, the original NP-hard problem is decoupled into a short-term stage problem seeking for the allocation of physical power and subcarrier and a long-term stage problem of task offloading and frequency scaling. The simulation results show that the proposed algorithm achieves excellent performance in energy saving in comparison with conventional schemes and realizes higher utilization of green energy by adjusting the energy price of MSs.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".