A Hybrid Scheduling Approach for Scalable Heterogeneous Hadoop Systems
Bibliographic record
Abstract
The scalability of Cloud infrastructures has significantly increased their applicability. Hadoop, which works based on a MapReduce model, provides for efficient processing of Big Data. This solution is being used widely by most Cloud providers. Hadoop schedulers are critical elements for providing desired performance levels. A scheduler assigns MapReduce tasks to Hadoop resources. There is a considerable challenge to schedule the growing number of tasks and resources in a scalable manner. Moreover, the potential heterogeneous nature of deployed Hadoop systems tends to increase this challenge. This paper analyzes the performance of widely used Hadoop schedulers including FIFO and Fair sharing and compares them with the COSHH (Classification and Optimization based Scheduler for Heterogeneous Hadoop) scheduler, which has been developed by the authors. Based on our insights, a hybrid solution is introduced, which selects appropriate scheduling algorithms for scalable and heterogeneous Hadoop systems with respect to the number of incoming jobs and available resources.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".