High Availability for virtualized Programmable Logic Controllers with Hard Real-Time Requirements on Cloud Infrastructures
Bibliographic record
Abstract
Cloud computing is becoming more popular in domains where previously hardware-based bare metal implementations dominated the field of computation workloads such as the automation and process industry. A variety of stateful applications exist that will require high availability on cloud infrastructures while also meeting the hard real-time requirements in the millisecond area of their superimposed processes, e.g., virtualized programmable logic controllers (vPLCs) and artificial intelligence inference services. This paper presents an approach for stateful applications on distributed systems to meet the application’s requirements in failover scenarios through state synchronization by means of Remote Direct Memory Access (RDMA). Experimental results with a software PLC confirm the effectiveness of the described approach in comparison to UDP-based synchronization, reducing the average synchronization time by up to 99.39%. The concept is suitable for applications on virtual machines and containers and might be an enabler for virtualization of real-time critical applications such as control functions in the automation and process industry.
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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 | 0.002 |
| 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 | 0.001 | 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".