On the Deployment Feasibility of Message Oriented Middlewares in Mission-critical Applications
Bibliographic record
Abstract
Mission-critical applications (MCA) like smart grid management, first-aid response, and tactical coordination in military search and rescue operations refer to applications that can pose a risk to human lives or cause extensive and catastrophic losses. The deployment and management of these applications need careful consideration to meet the stringent performance demand of resource-constrained environments. One way to achieve such performance and dependability demand is to adopt Message Oriented Middlewares (MOMs) (e.g., Apache Kafka, RabbitMQ) as they enable real-time data analytics and informed decision making. Despite their extensive usage in legacy business intelligent applications, little is known about their suitability for mission-critical applications. This paper fills that gap by first deploying and testing mission-critical applications on Apache Kafka and RabbitMQ. Then, we measure the performance, security, and reliability of the chosen MOMs to support MCA. The evaluation results confirm that Apache Kafka outperforms RabbitMQ, making it a potential candidate to deploy MCA. Specifically, Kafka requires 13x less bandwidth than RabbitMQ, which could be further reduced by 85% using effective parameter tuning. Our findings pave the way for MOMs to be adopted in MCAs to meet their stringent performance and dependability demands.
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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 | 0.004 | 0.022 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".