Bibliographic record
Abstract
Deploying applications in a multi-cloud environment can be challenging.The cloud providers face the challenge of optimally utilizing the cloud resources to achieve a certain goal such as reducing the power consumption of the cloud.The cloud users, who deploy applications in the cloud, face the challenge of satisfying the application requirements, such as the processing and memory requirements, and application user requirements, such as the throughput and response time constraint.There is a relationship between the challenges faced by the cloud providers and cloud users, which allows these challenges to be studied together.This work covers such a problem, which aims to find a deployment, i.e. allocate resources for an application in a multi-cloud environment, while satisfying the above requirements of the application, application user and cloud provider.The proposed algorithm uses a combination of queueing theory, clustering, graph partitioning and bin packing strategies to solve the multi-cloud application deployment problem.It produces a solution within 20 seconds in 90% of the test scenarios, which is a reasonable amount of time to be useful in practice.In 77% of the test scenarios, the solution's power consumption is within 10% of an unachievable theoretical lower bound, showing that the heuristic algorithm is extremely effective.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".