Performance analysis model for big data applications in cloud computing
Bibliographic record
Abstract
The foundation of Cloud Computing is sharing computing resources dynamically allocated and released per demand with minimal management effort. Most of the time, computing resources such as processors, memory and storage are allocated through commodity hardware virtualization, which distinguish cloud computing from others technologies. One of the objectives of this technology is processing and storing very large amounts of data, which are also referred to as Big Data. Sometimes, anomalies and defects found in the Cloud platforms affect the performance of Big Data Applications resulting in degradation of the Cloud performance. One of the challenges in Big Data is how to analyze the performance of Big Data Applications in order to determine the main factors that affect the quality of them. The performance analysis results are very important because they help to detect the source of the degradation of the applications as well as Cloud. Furthermore, such results can be used in future resource planning stages, at the time of design of Service Level Agreements or simply to improve the applications. This paper proposes a performance analysis model for Big Data Applications, which integrates software quality concepts from ISO 25010. The main goal of this work is to fill the gap that exists between quantitative (numerical) representation of quality concepts of software engineering and the measurement of performance of Big Data Applications. For this, it is proposed the use of statistical methods to establish relationships between extracted performance measures from Big Data Applications, Cloud Computing platforms and the software engineering quality concepts.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".