Time-Slotted Resource Allocation in a Two-User Computationally-Constrained Offloading System
Bibliographic record
Abstract
Computation offloading is a promising technique that enables mobile devices to utilize additional computation resources provided by the Mobile Edge Computing (MEC) paradigm to reduce the energy consumption and the latency required to complete a computational task. In order to exploit the offloading opportunity efficiently, the available communication and computation resources must optimally be allocated to the devices. In this paper, we seek to address the joint optimization problem of minimizing the total energy consumption of a two-user offloading system over limited communication and computation resources, under various multiple access schemes. We will propose an optimal time-slotted resource allocation strategy in which the resources are either shared or assigned to a single user in each slot. We will determine the optimal arrangement of the time slots and then obtain closed-form expressions for the jointly optimal slot lengths and resource allocations over all time slots. Our numerical results illustrate that the combination of the time-slotted structure and a capacity-approaching multiple access scheme enables the proposed resource allocation approach to significantly reduce the total energy consumption of an offloading system as compared to the existing approaches.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".