Joint Wireless and Service Allocation for Mobile Computation Offloading with Job Completion Time and Cost Constraints
Bibliographic record
Abstract
This paper proposes a method of joint wireless network and job service allocation for use with mobile computation offloading where task completion times have deadline constraints. In this design, mobile devices (MDs) may execute a computational task locally or offload the task through a wireless network for execution on an edge server (ES). The network owner offers to lease wireless communication channels at a given set of base stations along with edge server capacity that is used for job execution. The objective is to obtain a wireless and service capacity allocation that minimizes the total energy consumption of the mobile devices, subject to a cost budget constraint and constraints on the delay incurred by offloaded task execution. The design is first formulated as a mixed integer nonlinear programming problem. An approximate solution is then obtained by decomposing it into a collection of convex subproblems that can be efficiently solved. Results are presented that demonstrate that the proposed solution achieves near optimum performance over a wide range of system parameters.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".