Bibliographic record
Abstract
In a cloud environment, a scheduler assigns arriving tasks to one of many servers, with the goal of minimizing response times. There are two conventional approaches to cloud scheduling. The first is called the Join-the-Shortest-Queue (JSQ) algorithm, which directs an arriving task to the least loaded server. Despite its excellent delay performance, JSQ is throughput-limited, and thus doesn't scale well with the number of servers. The second is called the Power-of-d-choices (Pod) algorithm, which selects d servers at random and routes a task to the least loaded server of the d servers. Despite its scalability, Pod suffers from long tail response times. In this paper, a hybrid scheduling strategy is proposed, and it consists of a Pod scheduler and a throughput-limited helper. Hybrid scheduling takes the best of both worlds, enjoying scalability and low tail response times. In particular, hybrid scheduling has bounded maximum queue size in the large-system regime, which is in sharp contrast to the Pod scheduling whose maximum queue size is unbounded.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".