Two-phase ontology-based resource allocation approach for IaaS cloud service
Bibliographic record
Abstract
This paper proposes a composition-based resource allocation approach to cloud infrastructure-as-a-service (IaaS). A number of previous proposals have addressed resource allocation for IaaS services with bandwidth guarantee as the main obstacle to accessing datacenter-based cloud resources. Less focus has been put on utilization of hosting resources, i.e. computing and storage. Shortcomings with these proposals (i) may ensue in a high blocking of IaaS requests and (ii) a less efficient use of datacenter resources, which may impact a provider's revenue. In this research paper, a Two-Phase IaaS resource allocation approach (2P-IaaS) is proposed to address these shortcomings: (i) hosting resources mapping phase, and (ii) connectivity composition phase. This approach adopts a semantic ontology model with its associated reasoning capabilities to represent, discover and assign diverse cloud resources to IaaS requests. Furthermore, the proposed approach utilizes semantic similarity and closeness centrality to define an efficient cloud IaaS topology that connects the assigned resources. In this research, experiments on various sets of IaaS requests show significant advantages when compared with selected benchmarks.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".