PERIDOT: Modeling Execution Time of Spark Applications
Bibliographic record
Abstract
A data analytics application submitted to a Spark cluster often has to finish executing by a specified deadline. To use cluster resources effectively, the key challenge is having the ability to gain quick insights on how the execution time of any given application is likely to be impacted by the resources allocated to the application, e.g., the number of Spark executor cores and the size of the input data. Such insights can be used to quickly estimate the required resources needed for the desired execution time. Our paper proposes an automated execution time estimation approach called PERIDOT that involves executing a given application under a fixed resource setting with two small subsets of its input data to offer fast, lightweight execution time predictions. It analyzes these two executions to estimate the internal dependencies of the application and combines them with knowledge of Sparks data partitioning mechanisms to derive an analytic model that can estimate execution times for other resource settings and input data sizes. Our results from a wide range of applications and multiple Spark clusters show that PERIDOT can accurately estimate the execution time of an application from limited historical data, and suggest the minimum amount of resources required to meet an execution deadline.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".