SparkSim: Performance Modeling for Resource Allocation of Spark Data Science Projects
Bibliographic record
Abstract
Data science has emerged as an integral part of enterprise business operations, aiming to uncover data-driven insights. However, volatility and variability are two major issues of these systems. The latter indicates the need to consider a multitude of conditions when assessing the accuracy of predictions made by analytics systems. This complexity stems from the understanding that each of these circumstances can alter at any given time. Consequently, the challenge is to estimate the predictive accuracy of data science systems even in the midst of uncertainty and variability. Such an estimate can enable good planning and potentially dynamic resource allocation for such projects. In this work, we present a study of various machine learning and deep learning models to estimate the performance of data science projects deployed in Apache Spark, a popular and flexible distributed analytics platform. Moreover, we create experiments for the purpose of gathering data, as well as assessing feature importance to find which input configuration contributes the most. We demonstrate the process of training such a model, from data collection to training and testing, and we systematically compare the various alternatives to help decision-makers choose the best one. Thus, by providing insights into the performance of data science projects under uncertain and variable conditions, this work offers valuable contributions to both research and practical applications to make informed decisions, plan effectively, and allocate resources dynamically in data science projects. Our results show that LSTM and MLP outperform other models in terms of response time models and throughput models.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.003 | 0.001 |
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".