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    智能物联网技术赋能算网一体数据库的效能优化

    Efficiency Optimization for Computer Network Integrated Database Empowered by Artificial Intelligence of Things Technology

    • 摘要: 海量感知数据的采集、存储和共享技术推动了物联网的兴起,其大规模应用对数据库系统提出了数据强一致和资源高效能的迫切要求. 然而,现有的数据库系统架构和管控方法在保证一致性的约束下,较多采用降低传输数据量和增加节点存储副本的方式降低通信成本,缺少对系统网络资源的一体化感知和优化,从而导致数据库效能低下. 为此,构建算网一体数据库系统,驱动算力资源和网络的一体化感知表征,并将智能物联网(artificial intelligence of things,AIoT)技术赋能数据库,实现算力资源和网络的联合智能调度,以降低综合成本和提升算网效能. 首先,构建算网一体的分布式数据库网络,并分析其架构特点. 然后,为实现计算和通信优化变量的统一表征,构建了一体化代价感知模型. 并在此基础上提出了智能化的一站式资源优化算法,满足一致性时延约束的前提下,最优化数据库系统的整体算网效能. 最后,仿真实验验证了所提架构和算法在算网性能、算法收敛、集成代价和资源效率上均具有优越性.

       

      Abstract: The collection, storage and sharing technology of massive perceptual data has promoted the rise of the Internet of things. Its large-scale application has put forward an urgent requirement for database to have strong data consistency and high resource efficiency. However, the existing database system architecture and management and control methods mostly reduce communication costs by reducing the amount of data transmitted and increasing node storage replicas, and lack of integrated awareness and optimization of system network resources, resulting in low database efficiency. For this reason, we construct an integrated computing network database system to drive the integrated perception representation of computing resources and networks, and enables the database with intelligent Internet of things technology (AIoT) to realize the joint intelligent scheduling of computing resources and networks, so as to reduce the overall cost and improve the efficiency of computing networks. First, we construct a distributed database network integrated with computing and network, and analyze its architecture characteristics. Then, in order to realize the unified representation of computing and communication optimization variables, an integrated cost perception model is constructed. On this basis, an intelligent one-stop resource optimization algorithm is proposed to maximize the overall computing efficiency of the database system. Finally, simulation experiments verify the superiority of the proposed architecture and algorithm in network performance, algorithm convergence, integration cost and resource efficiency.

       

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