Adaptive Resource Dimensioning with Joint Workload Placement for Cloud Stack Layers
Bibliographic record
Abstract
Distributed cloud combines the best of telco and cloud technology and can run any application across multiple sites. Each site might be equipped with multiple virtualization layers (multi-layer cloud stack), e.g., OpenStack and/or Kubernetes. Workloads can be hosted in different virtualization layers. While this brings flexibility and elasticity for service provisioning, it also increases the complexity of life cycle management (LCM) during service instance design and assign. Especially, the compute and networking resources are typically overprovisioned due to a lack of an automated mechanism to scale them up/down or in/out according to their use. Adaptive resource dimensioning of cloud layers is needed to significantly smooth the process of service provisioning and to better utilize the virtualization layers’ resources. To address these challenges, in this paper, we propose a joint resource dimensioning and workload placement solution with multiple virtualization layers during service instance design and assign time. We demonstrate the feasibility of our solution with illustrative examples.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".