BigDataNetSim: a simulator for data and process placement in large big data platforms
Bibliographic record
Abstract
Big Data platforms are convoluted distributed systems which commonly comprise skill- and labour-intensive solution development to treat inherent Big Data application challenges. Several tools have been proposed to help developers and engineers to overcome the involved complexities in coordinating the execution of plenty processes/threads on multiple machines. However, no work so far has been able to combine both an accurate representation of Big Data jobs and realistic modeling of the behaviour of Big Data platforms at scale, including networking elements and data and job placement. In this paper, we propose BigDataNetSim, the first simulator which models accurately all the main components of the data movements in Big Data platforms (e.g., HDFS, YARN/MapReduce, network topologies, switching/routing protocols) in a large scale system. BigDataNetSim can serve as a valuable tool for engineering Big Data solutions, which includes set-up of systems, prototyping of jobs, and improvement of components/algorithms for Big Data platforms. We also demonstrate that BigDataNetSim can simulate a real Hadoop cluster with a high degree of accuracy in terms of data and job placements, being able to scale up to very large systems.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".