Automatic, On-Line Tuning of YARN Container Memory and CPU Parameters
Bibliographic record
Abstract
Big data analytic technologies such as Hadoop and Spark run on compute clusters that are managed by resource managers such as YARN. YARN manages resources available to individual applications, thereby affecting job performance. Manual tuning of YARN tuning parameters can result in sub-optimal and brittle performance. Parameters that are optimal for one job may not be well suited to another. In this paper we present KERMIT, the first on-line automatic tuning system for YARN. KERMIT optimizes in real-time YARN memory and CPU allocations to individual YARN containers by analysing container response-time performance. Unlike previous automatic tuning methods for specific systems such as Spark or Hadoop, this is the first study that focuses on the more general case of on-line, real-time tuning of YARN container density and how this affects performance of applications running on YARN. KERMIT employs the same tuning code to automatically tune any system that uses YARN, including both Spark and Hadoop. The effectiveness of our technique was evaluated for Hadoop and Spark jobs using the Terasort, TPCx-HS, and SMB benchmarks. KERMIT was able to achieve an efficiency of more than 92% of the best possible tuning configuration (exhaustive search of the parameter space) and up to 30% faster than basic manual tuning.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".