AdSCHE: DESIGN OF AN AUCTION-BASED FRAMEWORK FOR DECENTRALIZED SCHEDULING
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 it