An Investigation into the Usage-trends of Canada’s Research Computing Clouds
Bibliographic record
Abstract
Cloud platforms are gaining more footprint in many Information Technology sectors, including research computing. Infrastructure as a service (IaaS) remains a popular cloud delivery model for research computing clouds, enabling researchers to create isolated computing, storage, and network environments on demand. Despite the maturity of the IaaS model, cloud providers face several challenges due to the lack of insight into usage-trends of their platforms, including sub-optimal VM placement and arbitrary selection of adopted technologies. These problems become more difficult in the research computing domain, where the resource requirements and nature of computational workloads of VMs are highly diverse.In this paper, we illustrate the value of usage-trends' analysis in VM placement decisions and technology adoption choices by IaaS providers. We examine the usage-trends of four of Canada's research computing clouds, presenting our analysis of usage metrics that were collected since their inception and up to early 2019. We focus on examining the usage metrics with potential in guiding VM placement and technology adoption choices, namely the lifetime of VMs, the frequency of VMs' creation, and the VMs' resource allocations.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.017 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".