Openstack scheduler evaluation using design of experiment approach
Bibliographic record
Abstract
Cloud computing is a computing model which is essentially characterized by an on-demand and dynamic provisioning of computing resources. In this model, a cloud is a large-scale distributed system which leverages internet and virtualization technologies to provide computing resources as a service. Efficient, flexible and dynamic resource management is among the most challenging research issues in this domain. In this context, we present a study focusing on the dynamic behavior of the scheduling functionality of an Infrastructure-as-a-Service (IaaS) cloud, namely OpenStack Scheduler. We aim, through this study at identifying the limitations of this scheduler and ultimately enabling its extension using enhanced metrics. Towards this end, we present a Design of Experiment (DOE) based approach for the evaluation of the OpenStack Scheduler behavior. In particular, we use the screening type of experiment to identify the factors with significant effects on the responses. In our context, these factors are the amount of memory and the number of CPU cores assigned to virtual machine (VM) and the amount of memory and the number of cores on physical nodes. More specifically, we present a two-level fractional factorial balanced with the resolution IV and four center points experimental design with no replication.
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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 | 0.014 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".