Optimal Multi-Decision Mobile Computation Offloading With Hard Task Deadlines
Bibliographic record
Abstract
Multi-decision mobile computation offloading occurs when a task to be remotely executed is uploaded in separate parts. Since the upload is partitioned, separate decisions are needed to determine the best time to initiate each upload. The multi-decision problem is considered for the case where execution completion times are subject to hard deadline constraints and where task offloads occur over a Markovian wireless channel. An online energy-optimal computation offloading algorithm, Multiopt (Multi-decision online Optimum), is introduced, whose optimality is proven using Markovian stopping theory. The paper presents results using the Gilbert-Elliott channel model, where task completion time probabilities can be efficiently computed using Dynamic Programming. Although the proposed algorithm is proven to be energy optimal, its performance is also compared to four others, namely, Immediate Offloading, Channel Threshold, Local Execution, as well as optimal single-part offloading. Results show that the proposed algorithm can significantly improve mobile device energy consumption compared to the other approaches while guaranteeing hard task execution deadlines.
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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 | 0.000 |
| 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".