Optimal Task Offloading Policy in Edge Computing Systems with Firm Deadlines
Bibliographic record
Abstract
Task migration to remote servers offers a promising solution to the congestion issue in mobile edge computing systems. Our optimal task offloading design minimizes a system cost function, encompassing offloading and penalty costs. The offloading cost reflects external server resource usage, while the penalty cost accounts for task expiration risk. To optimize the expected cost over a time horizon, we employ Dynamic Programming (DP) and analyze its properties for an optimal offloading policy. “Curse of Dimensionality” of the DP equation poses computational challenges, especially with infinite state space. To mitigate this, we identify crucial policy properties, enabling DP evaluation on a finite state subset. Moreover, we show that the computation of the optimal task offloading decision at a given state can be deduced by leveraging the optimal decision taken at its “adjacent” states. We then provide numerical results to demonstrate parameter impact and validate theoretical findings.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".