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Open Access

ARTICLE

Enhanced Practical Byzantine Fault Tolerance for Service Function Chain Deployment: Advancing Big Data Intelligence in Control Systems

Peiying Zhang1,2,*, Yihong Yu1,2, Jing Liu3, Chong Lv1,2, Lizhuang Tan4,5, Yulin Zhang6,7,8
1 Qingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China), Qingdao, 266580, China
2 Shandong Key Laboratory of Intelligent Oil & Gas Industrial Software, Qingdao, 266580, China
3 Library of Shanghai Lixin University of Accounting and Finance, Shanghai, 201209, China
4 Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250014, China
5 Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing, Shandong Fundamental Research Center for Computer Science, Jinan, 250014, China
6 Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance of MOE, Minzu University of China, Beijing, 100081, China
7 Key Laboratory of Intelligent Game, Yangtze River Delta Research Institute of NPU, Taicang, 215400, China
8 Key Laboratory of Education Informatization for Nationalities (Yunnan Normal University), Ministry of Education, Kunming, 650092, China
* Corresponding Author: Peiying Zhang. Email: email
(This article belongs to the Special Issue: Big Data and Artificial Intelligence in Control and Information System)

Computers, Materials & Continua https://doi.org/10.32604/cmc.2025.064654

Received 20 February 2025; Accepted 03 April 2025; Published online 24 April 2025

Abstract

As Internet of Things (IoT) technologies continue to evolve at an unprecedented pace, intelligent big data control and information systems have become critical enablers for organizational digital transformation, facilitating data-driven decision making, fostering innovation ecosystems, and maintaining operational stability. In this study, we propose an advanced deployment algorithm for Service Function Chaining (SFC) that leverages an enhanced Practical Byzantine Fault Tolerance (PBFT) mechanism. The main goal is to tackle the issues of security and resource efficiency in SFC implementation across diverse network settings. By integrating blockchain technology and Deep Reinforcement Learning (DRL), our algorithm not only optimizes resource utilization and quality of service but also ensures robust security during SFC deployment. Specifically, the enhanced PBFT consensus mechanism (VRPBFT) significantly reduces consensus latency and improves Byzantine node detection through the introduction of a Verifiable Random Function (VRF) and a node reputation grading model. Experimental results demonstrate that compared to traditional PBFT, the proposed VRPBFT algorithm reduces consensus latency by approximately 30% and decreases the proportion of Byzantine nodes by 40% after 100 rounds of consensus. Furthermore, the DRL-based SFC deployment algorithm (SDRL) exhibits rapid convergence during training, with improvements in long-term average revenue, request acceptance rate, and revenue/cost ratio of 17%, 14.49%, and 20.35%, respectively, over existing algorithms. Additionally, the CPU resource utilization of the SDRL algorithm reaches up to 42%, which is 27.96% higher than other algorithms. These findings indicate that the proposed algorithm substantially enhances resource utilization efficiency, service quality, and security in SFC deployment.

Keywords

Big data; intelligent transformation; heterogeneous networks; service function chain; blockchain; deep reinforcement learning; trusted deployment

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