Energy efficient offloading for competing users on a shared communication channel
Bibliographic record
Abstract
In this paper we consider mobile users that employ computation offloading. In computational offloading, users can reduce energy consumption by executing jobs on a remote cloud server, rather than locally. In order to execute a job in the cloud, a mobile user must upload the job over a base station channel which is shared by all of the uploading users. The jobs are subject to hard deadline constraints, and since the channel quality may be different for each user, this may restrict the users ability to reduce energy usage. The system is modelled as a competitive game where each user is interested in minimizing its own energy use. The game is subject to the real-time constraints imposed by job execution deadlines, user specific channel bit rates, and the competition over the shared communication channel. The paper shows that for known classes of parameters, a game where each user independently adjusts its offload decisions always has a pure Nash equilibrium, and a Gauss-Seidel-like method for determining this equilibrium is presented. Results are then presented which illustrate that the system always converges to a Nash equilibrium using Gauss-Seidel. Data is presented which show the number of Nash equilibria that are found, the number of iterations required, and the quality of the solutions obtained. In particular, we find that the solutions perform well compared to a lower bound on total energy performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".