Network latency impact on performance of software deployed across multiple clouds
Why this work is in the frame
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Bibliographic record
Abstract
In computing, an cloud may be introduced close to some of the end users, to give faster service for very demanding applications. The transactions that require heavy processing capacity and longer processing times are seen as more suitable to be carried out at the cloud. Parts in the core and edge may then have to communicate, introducing associated network latencies. An application should be deployed across edge and core with the aim to reduce the overall effect of network latencies, in order to meet end user response time goals. In this paper, we use a Layered Queueing Network performance model to explore the impact of network latency and some possible deployment choices on the responsiveness of an application called HCAT (Home Care Aides Technology). The evaluations show that the use of the edge may cause performance degradation, rather than gain, for some kinds of applications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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 it