Population Game Based Energy and Time Aware Task Offloading for Large Amounts of Competing Users
Bibliographic record
Abstract
Computation offloading is envisioned as a promising solution to resource scarcity problem on mobile devices. Mobile users can offload computation intensive tasks to remote cloud with stronger capabilities. In order to execute tasks in cloud, mobile users have to upload computational data through cellular networks. When large amounts of mobile users in the same cell attempt to offload mobile tasks through the base station, the communication latencies for data transmissions may be high due to limited bandwidth resources. However, since the task completion times are constrained by hard deadlines, this restricts the feasible set of computational tasks that can be uploaded. In this paper, we propose a population game based approach to achieve efficient computation offloading for large amounts of competing mobile users, where each user is aimed to minimize his energy consumption. This game is subject to the task execution deadlines, user specific data rates, and the competition over the shared communication channel. We analyze the evolutionary dynamic of the game and show that the game always admits a Nash equilibrium. We then design a computation offloading mechanism that can achieve a Nash equilibrium of the game. Numerical results demonstrate that the proposed mechanism can achieve efficient computation offloading performance and scale well as the system size increases.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".