Bundling communication messages in large scale cloud environments
Bibliographic record
Abstract
Cloud computing has been receiving a growing attention due to its features on the provisioning of computing resources for distributed processing. A lot of interest lays on the enabled benefits of flexible and elastic management of cheap and reliable computing sources though virtualization. The cheap characteristic brought from virtualization helps data centers to host more than one operating system on any machine. However, the time-sharing nature of virtualization and the existence of more software layers lead to larger delays in execution and network communications. The bundling of network messages directed to the same destination can be used as means to reduce the number of network transfers. In this paper, the effect of different message bundling aspects closely related to cloud applications is investigated. Analysis shows that message bundling effectively enhances communication efficiency; however, it is directly influenced by parameters such as number of messages, length of the bundling cycle, and number processing units.! Thus, the highest performance gain is achieved by flexibly adjusting according bundling parameters towards data communication in large-scale cloud environments.
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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 | 0.000 | 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".