Usage Trends Aware VM Placement in Academic Research Computing Clouds
Bibliographic record
Abstract
Academic Research Computing Clouds are widely deployed worldwide by research institutions to support researcher's computations. While the nature of the hosted use-cases is diverse, literature and institutions' published guides point to highly parallel HPC workloads and state-heavy workloads as two popular use-cases in research-computing clouds. Additionally, our prior investigation has uncovered unique patterns in the users' VM-provisioning behaviors in four of Canada's research-computing clouds. These patterns, i.e., usage-trends, were generated by examining nearly 1 million VMs created by researchers over four and half years. The usage trends mimicked behaviors of provisioning highly parallel HPC workloads and state-heavy portals. In this paper, we exploit the knowledge of these usage trends to guide VM placement decisions in research-computing IaaS clouds. We propose a delayed-provisioning algorithm that postpones resource allocations for incoming VM requests in anticipation of running VMs termination, minimizing the number of active PMs and PM underutilization. We examine the performance of the proposed algorithm compared to other placement algorithms using a real-life validation dataset of nearly 850 thousand VMs. The results show significant improvements ranging from 7% to 33% reduction in the number of active PMs and up to 61% reduction in the number of hourly underutilized PMs.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".