Bibliographic record
Abstract
Decentralized scheduling is one of the newly emerged avenues in scheduling research. It is concerned with allocating resources to alternative possible uses over time, where competing uses are represented by autonomous agents. Compared with classical scheduling models, decentralized scheduling is characterized with the distribution of scheduling knowledge and control, which introduces new levels of complexities, namely the coordination complexity due to the interaction problems among agents and the mechanism design complexity due to the self-interested nature of agents. These complexities intertwine and need to be addressed concurrently. This paper presents an auction-based framework which tackles coordination and mechanism design complexities through integrating an iterative bidding protocol, a requirement-based bidding language, and a constraint-based winner determination approach. Without imposing a time window discretization on resources the requirement-based bidding language allows bidders to bid for the processing of a set of jobs with constraints. Prices can be attached to quality attributes of schedules. The winner determination algorithm uses a depth-first branch and bound search. A constraint directed scheduling procedure is used at each node to verify the feasibility of the allocation. The bidding procedure is implemented by an ascending auction protocol. Experimental results show that the proposed auction framework exhibits improved computational properties compared with the general combinatorial auctions. A case study of applying the framework to decentralized media content scheduling in narrowcasting is also presented.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".