Bibliographic record
Abstract
Nowadays, there is a big increase in the usage of data analytic applications and services because of the growth in the data produced from many different sources. The QoS properties such as response time, reliability and latency of these services are important factors to decide which services to select. As we know, the energy consumption is becoming a big issue as a result of IT expansion. Therefore, establishing a QoS-based web service selection approach that considers energy consumption as one of the essential QoS properties represents a significant step towards selecting the greener web service. This paper presents an experimental study of energy consumption and latency behavior of data mining algorithms running as web services. Our study shows that, there is a strong relation between the dataset properties such as dataset size, number of attributes, data type, and QoS attributes energy consumption and latency. Based on the findings from our study, a prediction system is built which can be used to predict the energy consumption and latency values for data mining web services on a given dataset, and then these services can be ranked according to their predicted energy and latency values. Experimental results show the effectiveness of our prediction and service selection system.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".