Be Fair and Be Selfish! Characterizing Deterministic Diffusive Load-Balancing Schemes with Small Discrepancy
Bibliographic record
Abstract
We consider the problem of deterministic distributed load balancing of indivisible tasks in the discrete setting. A set of n processors is connected into a d-regular symmetric network. In every time step, each processor exchanges some of the tasks allocated to it with each of their neighbours in the network. The goal is to minimize the discrepancy between the number of tasks on the most-loaded and the least-loaded processor as quickly as possible. In this model, the performance of load-balancing schemes obtained by rounding the continuous diffusion process up or down to the nearest integer was considered by Rabani et al. (1998), who showed that after T = O(log(Kn)/µ) steps any such scheme achieves a discrepancy of O(d log n/µ), where µ is the spectral gap of the transition matrix of the network graph, and K is the initial load discrepancy in the system. In this work, we identify natural additional conditions on the form of the discrete deterministic balancing scheme, which result in smaller values of discrepancy between maximum and minimum load. Specifically, we introduce the notion of a cumulatively fair load-balancing scheme, in which every node sends in total almost the same number of tasks along each of its outgoing edges during every interval of consecutive time steps, and not only at every single step. As our first main result, we prove that any
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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 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".