Automatic Scaling Mechanism of Intermodal EDI System under Green Cloud Computing
Bibliographic record
Abstract
EDI is a hot topic in the research of multimodal transportation informatization, which determines the exchange level of intermodal transportation information. However, its high cost, large system coupling degree and low performance threshold cannot adapt to mass data exchange in high concurrent environment. Therefore, a decentralized, scalable, distributed and efficient data exchange system is formed. It plays a key role in realizing the comprehensive sharing of interdepartmental intermodal information in the cloud environment. In order to solve the problem of mismatching between application load and computing resource capacity and realize on-demand resource allocation and low carbon emission, this paper proposes to build an Extensible EDI system (XEDI) based on MSA and studies the scaling mechanism in container environment. Based on the resource scheduling characteristics of container cloud and considering the distribution and heterogeneity of intermodal cloud computing platform from the perspective of resource allocation, the automatic scaling mechanism of XEDI is established, the scaling model is established, and the automatic scaling algorithm is proposed. For Dominant Resource Fairness for XEDI (XDRF) resource allocation algorithm and Dominant Resource Fairness for XEDI (CXDRF) based on carbon considering energy consumption, the CXDRF algorithm is proved by quantitative experiments to achieve system performance optimization on the basis of ensuring system reliability and effectively reducing energy consumption. XEDI can not only meet the demand of dynamic load and improve service quality, but also reduce resource occupation and save energy by releasing virtual resources when resource utilization rate is low. It has great research significance and practical value for mass data application under low energy consumption conditions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".