Bibliographic record
Abstract
Many modern day cloud services are composites of multiple smaller services working correctly together. This design has become increasingly prevalent due to the rise of the microservices application architecture, as well as service chaining in Network Function Virtualization (NFV). Future composite applications and services will be deployed on multi-tier clouds where their constituent microservices may be geographically spread over different regions. To optimize the delivery of such composites, the constituent microservices must be placed in locations where their clients, which may be other microservices, are able to meet certain QoS constraints. We propose an architecture and present a prototype system for incorporating network metrics into the auto-scaling and scheduling decisions of cloud management systems. Given a service with QoS constraints, our system monitors the network metrics (e.g. latency and bandwidth) of their clients. If a particular client is unable to receive the required latency or bandwidth of the service, our system auto-scales the service and strategically places the new instance(s) in a location capable of meeting the service quality, and re-directs traffic to the new instance.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".