Computing Resources Monitoring and Task Scheduling Design for Targeting a Massive Patent Data Fusion Cluster

With the maturity of cloud computing and distributed computing theory, the fusion of massive patent data increasingly relies on distributed clusters to efficiently process data. A simple system monitoring and data processing system is difficult to effectively improve the stability and resource utili...

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Bibliographic Details
Published in:	2024 IEEE 2nd International Conference on Control, Electronics and Computer Technology (ICCECT) pp. 1179 - 1183
Main Authors:	Qiang, Ding, Nan, Ji, Liangliang, Wang, Shaojie, Guo, Yaqin, Chen
Format:	Conference Proceeding
Language:	English
Published:	IEEE 26-04-2024
Subjects:	Cloud computing Cluster data fusion Data integration Distributed databases Maintenance Negative feedback Processor scheduling Regulation System monitoring Task scheduling
Online Access:	Get full text
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Summary:	With the maturity of cloud computing and distributed computing theory, the fusion of massive patent data increasingly relies on distributed clusters to efficiently process data. A simple system monitoring and data processing system is difficult to effectively improve the stability and resource utilization efficiency of the entire cluster. This article combines the monitoring subsystem and data processing subsystem to design a task scheduling capability with negative feedback regulation for the entire cluster, which can significantly reduce the probability of system crashes caused by resource depletion, improve the efficiency of computing resource utilization, and reduce operation and maintenance costs.
DOI:	10.1109/ICCECT60629.2024.10545698