Modeling and Simulation of Cloud Computing with iSPD
Bibliographic record
Abstract
Cloud Computing, enabled by technological enhancements and the trend for reduction of investments in IT’s physical infrastructure, is the major computing infrastructure nowadays. However, its heterogeneity makes difficult to know if the use of a given environment is efficient or not. In this context, the performance evaluation of cloud systems is useful both to clients, who need to find the best resource configuration for their applications, and to providers, who need to evaluate which scheduling and allocation policies of resources and virtual machines are most cost effective. Simulation is a good approach for this evaluation since it can be done offline. The known cloud computing simulators have issues related to their usability and modeling capability. This work extends the iconic approach for modeling and simulation offered by iSPD to cloud computing, adding icons for virtual machines and VMMs. Our results show that iSPD is faster than Cloudsim with equivalent accuracy, while providing an easier interface to model and simulate IaaS and PaaS environments.
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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 | 0.001 |
| 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".