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基于Kalman算法的大數(shù)據(jù)存儲架構(gòu)可擴展性優(yōu)化算法
網(wǎng)絡安全與數(shù)據(jù)治理 11期
韓鎮(zhèn)陽,張磊,,任冬
(武警陜西省總隊,,陜西西安710116)
摘要: 為了優(yōu)化大數(shù)據(jù)存儲架構(gòu)可擴展性能,提高大數(shù)據(jù)架構(gòu)資源利用率,,通過引入Kalman算法設計了一種大數(shù)據(jù)存儲架構(gòu)可擴展性優(yōu)化算法,。首先,綜合考慮大數(shù)據(jù)存儲架構(gòu)與多核環(huán)境內(nèi)存布局之間的兼容性,,設計架構(gòu)內(nèi)存布局,。其次,設計分布式共享內(nèi)存協(xié)議,,確保各個進程在訪問共享內(nèi)存時能夠正確地協(xié)同工作,,提高存儲架構(gòu)的容錯性。在此基礎上,,利用Kalman算法,,動態(tài)調(diào)整存儲節(jié)點的負載,進而優(yōu)化大數(shù)據(jù)存儲架構(gòu),,以提高其可擴展性,。實驗結(jié)果表明,應用該算法后,,大數(shù)據(jù)存儲架構(gòu)的資源利用率始終高于對照組,,均達到了96%以上,最高達到了98%,,架構(gòu)可擴展性優(yōu)化效果顯著,,服務器資源利用更充分,大規(guī)模數(shù)據(jù)處理更高效,。
中圖分類號:TP311
文獻標識碼:ADOI:10.19358/j.issn.2097-1788.2023.11.005
引用格式:韓鎮(zhèn)陽,,張磊,任冬.基于Kalman算法的大數(shù)據(jù)存儲架構(gòu)可擴展性優(yōu)化算法[J].網(wǎng)絡安全與數(shù)據(jù)治理,,2023,,42(11):25-28.
A scalability optimization algorithm for big data storage architecture based on Kalman algorithm
Han Zhenyang, Zhang Lei, Ren Dong
(Shanxi Provincial Corps of the Chinese People′s Armed Police Force, Xi′an 710116,China)
Abstract: In order to optimize the scalability performance of big data storage architecture and improve the resource utilization of big data architecture, a Kalman algorithm was introduced to design a scalability optimization algorithm for big data storage architecture. Firstly, considering the compatibility between big data storage architecture and multi core environment memory layout, design the architecture memory layout. Secondly, design a distributed shared memory protocol to ensure that various processes can work together correctly when accessing shared memory, and improve the fault tolerance of the storage architecture. On this basis, the Kalman algorithm is used to dynamically adjust the load of storage nodes and optimize the big data storage architecture to improve its scalability. The experimental results show that the resource utilization rate of the big data storage architecture is consistently higher than that of the control group, reaching over 96%, with a maximum of 98%. The scalability optimization effect of the architecture is significant, and the utilization of server resources is more sufficient, enabling more efficient processing of large-scale data.
Key words : Kalman algorithm; big data storage architecture; scalability optimization; shared memory protocol; node load

0引言

大數(shù)據(jù)存儲架構(gòu)是指在存儲、處理和分析大規(guī)模數(shù)據(jù)時所采用的技術(shù)架構(gòu),。從廣義角度分析,,大數(shù)據(jù)存儲架構(gòu)是用于提取和處理海量數(shù)據(jù)并針對業(yè)務目的進行分析整理的整體系統(tǒng),可視作基于機構(gòu)業(yè)務需求的大數(shù)據(jù)解決方案的藍圖[1],。大數(shù)據(jù)存儲架構(gòu)通常包括以下幾個主要組成部分:數(shù)據(jù)存儲層,、數(shù)據(jù)處理層、數(shù)據(jù)分析層和數(shù)據(jù)可視化層,。隨著大數(shù)據(jù)時代的來臨,,信息資源數(shù)據(jù)的體量越來越龐大,,大數(shù)據(jù)存儲架構(gòu)面臨著巨大的挑戰(zhàn)[2]。傳統(tǒng)的大數(shù)據(jù)存儲架構(gòu)通常采用中央式存儲方式,,這種方式在處理大規(guī)模數(shù)據(jù)時存在著很多局限性,,例如可擴展性差、容錯能力低等問題[3],。為了應對挑戰(zhàn),,研究者們提出了大數(shù)據(jù)存儲架構(gòu)可擴展性優(yōu)化算法,對大數(shù)據(jù)存儲架構(gòu)進行優(yōu)化,,以提高其性能和可擴展性,。當前,傳統(tǒng)的大數(shù)據(jù)存儲架構(gòu)可擴展性優(yōu)化算法在實際應用中以批處理為主,,缺乏實時的支撐,。面對需要快速響應和處理的應用場景,如實時分析,、實時推薦等,,仍然存在缺陷,且對業(yè)務支撐的靈活度效果不佳[4],。

Kalman算法是一種優(yōu)秀的估計算法,,它具有很好的自適應性和魯棒性,能夠?qū)碗s系統(tǒng)進行準確的估計和預測[5],。在大數(shù)據(jù)存儲架構(gòu)中,,Kalman算法可以用于數(shù)據(jù)的優(yōu)化和預測,采用分布式存儲方式,,通過將數(shù)據(jù)分散到多個節(jié)點上進行存儲和處理,,提高數(shù)據(jù)的可擴展性和容錯能力,提高數(shù)據(jù)存儲和處理的效率,?;诖耍疚囊隟alman算法來開展大數(shù)據(jù)存儲架構(gòu)可擴展性優(yōu)化算法研究,。


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作者信息:

韓鎮(zhèn)陽,,張磊,,任冬

 (武警陜西省總隊,,陜西西安710116)


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