Model driven performance simulation of cloud provisioned Hadoop mapreduce applications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Hadoop is a widely adopted open source implementation of MapReduce. A Hadoop cluster can be fully provisioned by a Cloud service provider to provide elasticity in computational resource allocation. Understanding the performance characteristics of a Hadoop job can help achieve an optimal balance between resource usage (cost) and job latency on a cloud-based cluster. This paper presents a method that estimates the performance of a MapReduce application in a Cloud provisioned Hadoop cluster. We develop a model-driven approach that models a cloud provided independent Hadoop MapReduce model and customizes it for a specific Cloud deployment. These models are further transformed into a simulation model that produces estimations of end-to-end job latency. We explore this method in the design space of MapReduce applications to estimate the performance for different sizes of input data. Our approach provides a model-to-simulation-to-prediction method for observing the performance behaviour of MapReduce applications given a configuration of a MapReduce platform.
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.
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 | 0.001 |
| Open science | 0.001 | 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 it