MCSA: A multi-criteria shuffling algorithm for the MapReduce framework
Bibliographic record
Abstract
During the shuffle stage of the MapReduce framework, a large volume of data may be relocated to the same destination at the same time. This, in turn, may lead to the network hotspot problem. On the other hand, it is always more effective to achieve better data locality by moving the computation closer to the data than the other way around. However, doing this may result in the partitioning skew problem, which is characterized by the unbalanced computational loads between the destinations. Consequently, shuffling algorithms should consider all the following criteria: data locality, partitioning skew, and network hotspot. In order to do so, we introduce MCSA, a Multi-Criteria shuffling algorithm for the MapReduce scheduling stage that rests on three cost functions to accurately reflect the trade-offs between these different criteria. Extensive simulations were conducted and their results show that the MCSA-based scheduler consistently outperforms other schedulers based on these criteria. Furthermore, the MCSA-based scheduler can be easily adjusted to the meet the distinct needs of different customers.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".