Smart Data Prefetching Using KNN to Improve Hadoop Performance
Bibliographic record
Abstract
Abstract Hadoop is an open-source framework that enables the parallel processing of large data sets across a cluster of machines. It faces several challenges that can lead to poor performance, such as I/O operations, network data transmission, and high data access time. In recent years, researchers have explored prefetching techniques to reduce the data access time as a potential solution to these problems. Nevertheless, several issues must be considered to optimize the prefetching mechanism. These include launching the prefetch at an appropriate time to avoid conflicts with other operations and minimize waiting time, determining the amount of prefetched data to avoid overload and underload, and placing the prefetched data in a location that can be accessed efficiently when required. In this paper, we propose a smart prefetch mechanism that consists of three phases designed to address these issues. First, we enhance the task progress rate to calculate the optimal time for triggering prefetch operations. Next, we utilize K-Nearest Neighbor (KNN) clustering to identify which data blocks should be prefetched in each round, employing the data locality feature to determine the placement of prefetched data. Our experimental results demonstrate that our proposed smart prefetch mechanism improves job execution time by an average of 28.33% by increasing the rate of local tasks.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".