An Asynchronous Fixed-Point Algorithm for Resource Sharing With Coupled Objectives
Bibliographic record
Abstract
Distributed resource allocation and sharing can often be formulated as a utility maximization problem, with the objective being the sum of user utilities minus a coupled cost. A traditional distributed solution to such problems, called “consistency pricing,” decouples the objective function via dual decomposition, which is then iteratively solved by the subgradient method. However, such gradient-based approaches may require many iterations of message passing to converge, which may not be sufficient in large-scale real-time applications. In this paper, we propose a new fixed-point-like distributed solution to resource sharing problems with coupled objective functions. While preserving the simple pricing interpretation, our approach speeds up convergence by exploiting the structural difference between user utilities and the coupled cost function. We theoretically analyze the asynchronous algorithm convergence conditions based on contraction mapping. Through a detailed case study of cloud bandwidth reservation based on real-world workload traces, we demonstrate the benefits of the proposed algorithm over state-of-the-art distributed optimization techniques including gradient descent, dual decomposition, and ADMM. In addition, we also extend the proposed algorithm to approach a more general class of consensus optimization problems with not only a coupled objective function, but also a certain class of coupled constraints.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".