Bibliographic record
Abstract
Hadoop is a cloud computing open source system, used in large-scale data processing. It became the basic computing platforms for many internet companies. With Hadoop platform users can develop the cloud computing application and then submit the task to the platform. Hadoop has a strong fault tolerance, and can easily increase the number of cluster nodes, using linear expansion of the cluster size, so that clusters can process larger datasets. However Hadoop has some shortcomings, especially in the actual use of the process of exposure to the MapReduce scheduler, which calls for more researches on Hadoop scheduling algorithms.This survey provides an overview of the default Hadoop scheduler algorithms and the problem they have. It also compare between five Hadoop framework scheduling algorithms in term of the default scheduler algorithm to be enhanced, the proposed scheduler algorithm, type of cluster applied either heterogeneous or homogeneous, methodology, and clusters classification based on performance evaluation. Finally, a new algorithm based on capacity scheduling and use of perspective resource utilization to enhance Hadoop scheduling is proposed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".