A Distributed Intelligent Service Trusted Provision Approach for IoT

• Published in: IEEE internet of things journal Vol. 10; no. 24; p. 1
• Main Authors: Qi, Yuanyuan, Shao, Sujie, Wu, Shuang, Qiu, Xuesong, Guo, Song, Guo, Shaoyong
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 15.12.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Blockchain; Blockchains; Computer architecture; consensus mechanism; Convergence; Costs; Cryptography; Delays; Fault tolerance; Internet of Things; Microservice architectures; microservice orchestration; Neural networks; Resource management; Resource scheduling; resource sharing
• ISSN: 2327-4662; 2327-4662
• DOI: 10.1109/JIOT.2023.3303927

The traditional centralized resource scheduling method leads to trust issues among multiple subjects carrying microservices. At the same time, in the process of service provision, single point failure problems also occur from time to time. In order to realize the trusted provision of services, we build a blockchain-based distributed intelligent service trusted provision architecture, which uses smart contracts to realize on-chain registration of resources information and automatic orchestration of microservices. In order to break through the bottleneck of blockchain efficiency and improve scalability, we use sharding technology to expand the blockchain. And the Raft-PBFT two-level consensus mechanism combining Boneh-Lynn-Sacham (BLS) threshold signature (B-RBFT) is designed for blockchain sharding, which greatly improves throughput and reduces consensus delay, while taking security into account. To meet higher quality of service (QoS) requirements, we design the microservice orchestration algorithm based on improved double deep Q network (DDQN) to support microservice deployment and migration. In particular, to make the neural network converge faster, we improve the traditional DDQN framework by using double replay buffers and weighted target values. Simulation results show that our proposed algorithm has advantages in convergence speed, resource usage cost, delay and load balancing.