Modeling and Performance Evaluation of Streaming Data Processing System in IoT Architecture
Feng Zhu*, Kailin Wu, Jie Ding
School of Computer, Jiangsu University of Science and Technology, Zhenjiang, 212100, China
* Corresponding Author: Feng Zhu. Email: 
Computers, Materials & Continua https://doi.org/10.32604/cmc.2025.062007
Received 08 December 2024; Accepted 17 February 2025; Published online 11 March 2025
Abstract
With the widespread application of Internet of Things (IoT) technology, the processing of massive real-time streaming data poses significant challenges to the computational and data-processing capabilities of systems. Although distributed streaming data processing frameworks such as Apache Flink and Apache Spark Streaming provide solutions, meeting stringent response time requirements while ensuring high throughput and resource utilization remains an urgent problem. To address this, the study proposes a formal modeling approach based on Performance Evaluation Process Algebra (PEPA), which abstracts the core components and interactions of cloud-based distributed streaming data processing systems. Additionally, a generic service flow generation algorithm is introduced, enabling the automatic extraction of service flows from the PEPA model and the computation of key performance metrics, including response time, throughput, and resource utilization. The novelty of this work lies in the integration of PEPA-based formal modeling with the service flow generation algorithm, bridging the gap between formal modeling and practical performance evaluation for IoT systems. Simulation experiments demonstrate that optimizing the execution efficiency of components can significantly improve system performance. For instance, increasing the task execution rate from 10 to 100 improves system performance by 9.53%, while further increasing it to 200 results in a 21.58% improvement. However, diminishing returns are observed when the execution rate reaches 500, with only a 0.42% gain. Similarly, increasing the number of TaskManagers from 10 to 20 improves response time by 18.49%, but the improvement slows to 6.06% when increasing from 20 to 50, highlighting the importance of co-optimizing component efficiency and resource management to achieve substantial performance gains. This study provides a systematic framework for analyzing and optimizing the performance of IoT systems for large-scale real-time streaming data processing. The proposed approach not only identifies performance bottlenecks but also offers insights into improving system efficiency under different configurations and workloads. The code and experimental results are accessible at (accessed on 10 January 2025).
Keywords
System modeling; performance evaluation; streaming data process; IoT system; PEPA
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