An adaptive scheduling algorithm for dynamic heterogeneous Hadoop systems
Bibliographic record
Abstract
The MapReduce and Hadoop frameworks were designed to support efficient large scale computations. There has been growing interest in employing Hadoop clusters for various diverse applications. A large number of (heterogeneous) clients, using the same Hadoop cluster, can result in tensions between the various performance metrics by which such systems are measured. On the one hand, from the service provider side, the utilization of the Hadoop cluster will increase. On the other hand, from the client perspective the parallelism in the system may decrease (with a corresponding degradation in metrics such as mean completion time). An efficient scheduling algorithm should strike a balance between utilization and parallelism in the cluster to address performance metrics such as fairness and mean completion time. In this paper, we propose a new Hadoop cluster scheduling algorithm, which uses system information such as estimated job arrival rates and mean job execution times to make scheduling decisions. The objective of our algorithm is to improve mean completion time of submitted jobs. In addition to addressing this concern, our algorithm provides competitive performance under fairness and locality metrics (with respect to other well-known Hadoop scheduling algorithms - Fair Sharing and FIFO). This approach can be efficiently applied in heterogeneous clusters, in contrast to most Hadoop cluster scheduling algorithm work, which assumes homogeneous clusters. Using simulation, we demonstrate that our algorithm is a very promising candidate for deployment in real systems.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".