Oh-Trust: Overbooking and Hybrid Trading-Empowered Resource Scheduling With Smart Reputation Update Over Dynamic Edge Networks
Bibliographic record
Abstract
Incentive-driven computing resource sharing is crucial for meeting the ever-growing demands of emerging mobile applications. Although conventional spot trading offers a solution, it frequently leads to excessive overhead due to the need for real-time trading related interactions. Likewise, traditional futures trading, which depends on historical data, is susceptible to risks from network dynamics. This paper explores a dynamic and uncertain edge network comprising a computing platform, e.g., an edge server, that offers computing services as resource seller, and various types of mobile users with diverse resource demands as buyers, including fixed buyers (FBs) and uncertain occasional buyers (OBs) with fluctuating needs. To facilitate efficient and timely computing services, we propose an overbooking- and hybrid trading-empowered resource scheduling mechanism with reputation update, termed Oh-Trust. Particularly, our Oh-Trust incentivizes FBs to enter futures trading by signing long-term contracts with the seller, while simultaneously attracting OBs to spot trading, enhancing resource utilization and profitability for both parties. Crucially, to adapt to market fluctuations, a smart reputation updating mechanism is integrated, allowing for the timely renewal of long-term contracts to optimize trading performance. Extensive simulations using real-world datasets demonstrate the effectiveness of Oh-Trust across multiple evaluation metrics.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".