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This research paper explores the design and development of a sophisticated event-driven monitoring system customized for Wi-Fi 6 Access Points (APs), utilizing Java-based microservices architecture. Wi-Fi 6 marks a breakthrough in wireless technology, providing enhanced speed and efficiency in environments with high network traffic. The proposed system seeks to take advantage of these advancements by implementing real-time event processing and Kafka messaging infrastructure to improve network management. The system’s architecture is constructed to support comprehensive monitoring through several essential components. Event listeners detect Quality of Experience (QoE) events and Trap events generated by the APs, which are then communicated via Kafka topics. These topics are fundamental to the real-time interaction between services, ensuring smooth data transmission and processing. The system consists of multiple microservices that manage different aspects of event processing, from initial detection to detailed analysis. This research paper offers a detailed exploration of the system’s architecture, including the integration and performance of its components. It also examines the system’s ability to efficiently manage and process various event types, such as AP status updates and user-generated reports. Key elements of the system, including error handling and debugging strategies, are discussed, particularly in cases involving events with unclear or ambiguous syntax. A key part of the study involves a performance comparison between the Quarkus and Spring Boot frameworks. The paper evaluates and contrasts startup times and message processing efficiency, offering insights into how each framework performs in managing Kafka-based event-driven processing. By assessing these metrics, the research aims to provide valuable insights into the field of network monitoring and microservices architectures, emphasizing the benefits and challenges of each framework in real-time applications. Overall, this research advances the understanding of network monitoring solutions by illustrating the effectiveness of microservices and event-driven design, and by offering a comparative analysis of two leading Java frameworks in the context of real-time event processing.

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Comparison of Quarkus and Spring Boot Microservices with Integration of Kafka in Access Point Management and Streaming

  • 会议论文

  • 发表日期：

    2026年

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  • Manoj Bhat,
  • H. Vishalakshi Prabhu

摘要

This research paper explores the design and development of a sophisticated event-driven monitoring system customized for Wi-Fi 6 Access Points (APs), utilizing Java-based microservices architecture. Wi-Fi 6 marks a breakthrough in wireless technology, providing enhanced speed and efficiency in environments with high network traffic. The proposed system seeks to take advantage of these advancements by implementing real-time event processing and Kafka messaging infrastructure to improve network management. The system’s architecture is constructed to support comprehensive monitoring through several essential components. Event listeners detect Quality of Experience (QoE) events and Trap events generated by the APs, which are then communicated via Kafka topics. These topics are fundamental to the real-time interaction between services, ensuring smooth data transmission and processing. The system consists of multiple microservices that manage different aspects of event processing, from initial detection to detailed analysis. This research paper offers a detailed exploration of the system’s architecture, including the integration and performance of its components. It also examines the system’s ability to efficiently manage and process various event types, such as AP status updates and user-generated reports. Key elements of the system, including error handling and debugging strategies, are discussed, particularly in cases involving events with unclear or ambiguous syntax. A key part of the study involves a performance comparison between the Quarkus and Spring Boot frameworks. The paper evaluates and contrasts startup times and message processing efficiency, offering insights into how each framework performs in managing Kafka-based event-driven processing. By assessing these metrics, the research aims to provide valuable insights into the field of network monitoring and microservices architectures, emphasizing the benefits and challenges of each framework in real-time applications. Overall, this research advances the understanding of network monitoring solutions by illustrating the effectiveness of microservices and event-driven design, and by offering a comparative analysis of two leading Java frameworks in the context of real-time event processing.