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A Blockchain System for QoS Monitoring in Decentralized Edge Computing

块链 计算机科学 边缘计算 分布式计算 服务质量 GSM演进的增强数据速率 计算机网络 计算机安全 电信
作者
Puwei Wang,Haoran Li,Hang Fu,Zhouxing Sun,Jinchuan Chen,Xiaoyong Du
出处
期刊:IEEE Transactions on Services Computing [Institute of Electrical and Electronics Engineers]
卷期号:17 (1): 263-276 被引量:3
标识
DOI:10.1109/tsc.2023.3345634
摘要

In edge computing, applications are usually delivered as services, each of which runs independently and cooperates to construct complicated applications. QoS (Quality of Service) monitoring is an important way to detect and locate faulty services. In a decentralized environment, QoS monitoring will face trust problem because it is difficult to guarantee the trustworthiness of monitoring results. This article builds a blockchain system for QoS monitoring. However, there are two challenges. First, although the blockchain consensus ensures the consistency of on-chain data among nodes, there is no guarantee that the monitoring data collected in the decentralized environment are authentic, because malicious nodes may report falsified data. Second, in order to handle service faults in time, the real-time query is usually required for obtaining monitoring data. But, blockchains suffer from inefficient querying, because the sequential data storage of blockchain is designed for write intensive applications at the expense of some read performance. To address these challenges, this article proposes a clustering-based algorithm for validating the authenticity of monitoring data collected in the decentralized environment, and proposes a probabilistic threshold query over blockchain, which supports efficient querying and guarantees the probability that the query results are correct is not less than a given threshold. This article implements the proposed blockchain system based on the blockchain platform Hyperledger Fabric and the edge computing platform KubeEdge . The experiment results demonstrate the proposed blockchain system provides high-throughput and low-latency monitoring ability, and can efficiently obtain monitoring results close to real QoS data.
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