A Reliable Embedding Framework for Elastic Virtualized Services in the Cloud
Bibliographic record
Abstract
This paper proposes a novel framework for managing the resource provisioning of reliable virtual networks (VN) in the cloud. This includes handling the placement of VN requests while providing availability guarantees, as well as reconfiguring/adapting their placement as their request changes over time. This is particularly interesting for services with periodic resource demands. Given the heterogeneous failure rates of physical network components, the placement and reconfiguration must ensure that the selected hosts for each VN meets its availability requirements. The existing work on availability-aware VN placement has overlooked the case of “availability over-provisioning,” as well as the fact that VN requests are subject to change over time. To this extent, we propose a novel framework that consists of two main modules; JENA: a tabu-based availability-aware resource allocation (embedding) module for VNs that achieves “just-enough” availability guarantees, and ARES: a reliable reconfiguration module to adapt the embedding of hosted services as they scale. Further, we introduce the concept of “protection-domains” and “protection-policies” to equip our proposed modules with the ability to augment services with redundant/backup nodes to enhance their reliability. Our numerical results show that our framework enhances network's admissibility (with 33% lower blocking compared to existing work), and in return increases the cloud provider's long term revenue, compared to peer and benchmark algorithms.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".