In-memory database load balancing optimization for massive information processing of the Internet of Things

IF 1.8 4区计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

ACM Transactions on Asian and Low-Resource Language Information Processing Pub Date : 2024-06-10 DOI:10.1145/3670996

Huixiang Xu

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引用次数: 0

Abstract

In order to improve the operation effect of the in-memory database for massive information processing of the Internet of Things, this paper combines the load balancing signal processing algorithm to carry out the load balancing optimization analysis of the in-memory database. According to the local transformation characteristics of non-stationary multi-component signals, an adaptive FSST algorithm is proposed in this paper. According to the signal separability condition, this paper uses the local Rayleigh entropy to estimate the window function parameters of the adaptive FSST and the adaptive FSST2. In addition, this paper adopts an adaptive window function to automatically match the local changes of the signal, so that the signal has the optimal energy aggregation in any part. The results show that when the number of concurrent users is the same, the time consumption, throughput and bandwidth of the proposed method are always higher than the method proposed in reference [10]. When the number of concurrent books is 97, the time of the proposed method is 45000ms, the time of the proposed method is 40000ms, the highest throughput of the proposed method is 2.30 MB/s, the highest bandwidth is 11.9MB/s, the highest throughput of the method proposed in reference [10] is 2.2 MB/s, and the highest bandwidth is 11.8MB/s. The load balancing optimization algorithm of the memory database for massive information processing of the Internet of Things has good results.

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针对物联网海量信息处理的内存数据库负载平衡优化

为了提高内存数据库在物联网海量信息处理中的运行效果，本文结合负载均衡信号处理算法，对内存数据库进行了负载均衡优化分析。根据非平稳多分量信号的局部变换特性，本文提出了一种自适应 FSST 算法。根据信号可分性条件，本文利用局部瑞利熵来估计自适应 FSST 和自适应 FSST2 的窗函数参数。此外，本文还采用自适应窗函数自动匹配信号的局部变化，使信号在任意部分都具有最优的能量聚集。结果表明，当并发用户数相同时，本文提出的方法的耗时、吞吐量和带宽始终高于参考文献[10]中提出的方法。当并发本数为 97 本时，所提方法的耗时为 45000ms，所提方法的耗时为 40000ms，所提方法的最高吞吐量为 2.30MB/s，最高带宽为 11.9MB/s，参考文献[10]所提方法的最高吞吐量为 2.2MB/s，最高带宽为 11.8MB/s。面向物联网海量信息处理的内存数据库负载均衡优化算法效果良好。

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来源期刊

ACM Transactions on Asian and Low-Resource Language Information Processing Computer Science-General Computer Science

CiteScore

3.60

自引率

15.00%

发文量

241

期刊介绍： The ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) publishes high quality original archival papers and technical notes in the areas of computation and processing of information in Asian languages, low-resource languages of Africa, Australasia, Oceania and the Americas, as well as related disciplines. The subject areas covered by TALLIP include, but are not limited to: -Computational Linguistics: including computational phonology, computational morphology, computational syntax (e.g. parsing), computational semantics, computational pragmatics, etc. -Linguistic Resources: including computational lexicography, terminology, electronic dictionaries, cross-lingual dictionaries, electronic thesauri, etc. -Hardware and software algorithms and tools for Asian or low-resource language processing, e.g., handwritten character recognition. -Information Understanding: including text understanding, speech understanding, character recognition, discourse processing, dialogue systems, etc. -Machine Translation involving Asian or low-resource languages. -Information Retrieval: including natural language processing (NLP) for concept-based indexing, natural language query interfaces, semantic relevance judgments, etc. -Information Extraction and Filtering: including automatic abstraction, user profiling, etc. -Speech processing: including text-to-speech synthesis and automatic speech recognition. -Multimedia Asian Information Processing: including speech, image, video, image/text translation, etc. -Cross-lingual information processing involving Asian or low-resource languages. -Papers that deal in theory, systems design, evaluation and applications in the aforesaid subjects are appropriate for TALLIP. Emphasis will be placed on the originality and the practical significance of the reported research.

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