An adaptive scheduling algorithm for dynamic heterogeneous Hadoop systems
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it