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September 5, 2025Open Access

Adaptive Kafka Resource Allocation for Real-Time Monitoring in the Manufacturing Sector

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Throughput targets were consistently met with the tuned Kafka configuration under diverse loads, ensuring efficiency.
The study identified limitations in consumer scaling under extreme loads, resulting in message processing halts.
The framework utilizes an algorithm named Bromin and KEDA for effective consumer autoscaling and resource management.
Practical application was demonstrated in a real-time monitoring and alert system, highlighting its operational impact.

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The adaptive framework improves real-time data processing and consumer scaling in manufacturing, suggesting effective resource allocation strategies.

Lokeswara et al. (2025) studied this question.

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The adaptive framework improves real-time data processing and consumer scaling in manufacturing, suggesting effective resource allocation strategies.