End-to-End Performance Prediction for Selecting Cloud Services Solutions
Bibliographic record
Abstract
In cloud computing, in order to select or recommend the best service solutions to end users, the end-to-end QoS requirements (e.g. response time and throughput) have to be computed. A typical cloud solution is a combination of multiple component services such as IaaS, SaaS, PaaS, etc. In a simplified case, there could be two components- software services and infrastructure services. The software service alone can satisfy end user's functional requirements (e.g. business objectives); however, the end-to-end QoS requirements require a collaboration of the multiple components at multiple cloud layers. In this paper, we consider the multilayered cloud architecture for computing the end-to-end performance values for cloud solutions. We propose a new method for measuring cloud component services similarity and predicting the end-to-end performance values of cloud solutions. In this method, the historical performance data of cloud component services is used based on users' past invocations. To evaluate our method and show its effectiveness, series of experiments are conducted. The experimental results demonstrate that our cloud multi-layers based method produces better prediction accuracy than other prediction approaches that consider one cloud layer.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".