Modeling and Performance Evaluation of Streaming Data Processing System in IoT Architecture  

作　　者：Feng Zhu Kailin Wu Jie Ding 

机构地区：[1]School of Computer,Jiangsu University of Science and Technology,Zhenjiang,212100,China

出　　处：《Computers, Materials & Continua》2025年第5期2573-2598,共26页计算机、材料和连续体(英文)

基　　金：funded by the Joint Project of Industry-University-Research of Jiangsu Province(Grant:BY20231146).

摘　　要：With the widespread application of Internet of Things(IoT)technology,the processing of massive realtime streaming data poses significant challenges to the computational and data-processing capabilities of systems.Although distributed streaming data processing frameworks such asApache Flink andApache 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 algorithmis introduced,enabling the automatic extraction of service flows fromthe PEPAmodel 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 perfor

关 键 词：System modeling performance evaluation streaming data process IoT system PEPA 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]