Redundant data filtering method oriented to cloud computing monitoring system

Abstract

The invention discloses a redundant data filtering method oriented to a cloud computing monitoring system. The method takes historical monitoring data as a reference to dynamically set out a reasonable threshold value, so that a data node preprocesses own monitoring data, and the pushing of the redundant data is minimized. The method solves the problems that network resources are greatly consumed, the data processing task load of a management node is increased and network congestion and system performance bottleneck are always caused since node resources are continuously monitored from multiple aspects and multiple levels under a large-scale cloud computing monitoring environment. The method gives a model of a redundant data filtering method and working steps of filtering data. On the premise that data accuracy and instantaneity is guaranteed, the redundant data is effectively filtered, network resource consumption is lowered, monitoring data storage space is reduced, the work load of the management node is lightened, so that the management node has short response time, and node faults are timely found.

Description

technical field [0001] The invention relates to the application field of information technology data management, in particular to a node resource monitoring data filtering method for a cloud computing monitoring system. Background technique [0002] Cloud computing provides users with dynamic, cost-effective, and elastically scalable computing, storage, and network services through the Internet through resources such as infrastructure, software, or platforms in an on-demand, pay-as-you-go manner. Cloud computing uses relatively concentrated resources to run decentralized applications and flexibly provide users with basic information resources. [0003] At present, the scale of newly-built cloud computing data centers is often relatively large. Some cloud data centers, such as those built by Baidu, Amazon, and Alibaba, often have tens of thousands of servers. There is a huge work pressure. How to improve the service quality of cloud computing has always been the goal pursue...

AI Technical Summary

Problems solved by technology

However, these solutions only focus on how to reduce the amount of data, thereby reducing system overhead, but the long collection period leads to many important resource changes being missed, which seriously affects the accuracy of monitoring data, and the monitoring data received by the management node cannot reflect the real situation. Changes in data node resources

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