Multi-level load balancing cloud resource monitoring method

Abstract

A multi-level load balancing cloud resource monitoring method, comprising: establishing a distributed multi-layer monitoring structure; analyzing the resource status of each monitoring node and automatically generating each resource monitoring script in combination with the requirements specified by a cloud platform user; a cluster central node integrates active and passive hosts The discovery strategy continuously monitors the responsible local area network; the monitoring center node adjusts the transmission protocol and other monitoring strategies in real time according to the load status of each cluster center node, uses fast compression and two-way transmission technology to reduce the transmission overhead of monitoring data, and switches in time when the center node fails Cluster center node; the monitoring center node regularly mines and analyzes the historical monitoring data of each resource, and conducts early warning and in-depth monitoring. According to the characteristics of a large-scale distributed cloud environment, it automatically discovers new nodes in the cluster, and relies on load balancing technology to give it a powerful fault-tolerant mechanism and event engine, thus ensuring the high reliability and security of the cloud environment.

Description

technical field [0001] The invention relates to cloud computing technology, in particular to a multi-level load balancing cloud resource monitoring method supporting active discovery. Background technique [0002] With the continuous deepening of the Internet era, the era of cloud computing and big data has arrived, the traditional IT architecture can no longer support the massive data generated, and the scale of data centers is showing an explosive growth trend. The traditional physical resource monitoring method can no longer meet the current needs, and a large amount of human operation and maintenance costs have brought a serious burden to the enterprise. In this situation, enterprises have replaced traditional IT infrastructure with cloud environments, but whether large-scale flat cloud resources are safe and reliable, and whether the overall performance of cloud services is as stable and reliable as traditional physical servers, requires a powerful The cloud monitoring...

AI Technical Summary

Problems solved by technology

OpenStack's Ceilometer, as a community open source project, has only recently been officially released to OpenStack in a stable version. The stability and reliability have yet to be verified. So far, Ceilometer is limited by OpenStack and has poor compatibility.

Nagios provides a powerful monitoring framework, a complete alarm system and fault-tolerant control, but there are fewer component monitoring items, and currently there is no persistent storage of alarm data and a friendly graphical interface display

Ganglia provides a powerful monitoring mechanism, rich monitoring items, flexible monitoring topology, and perfect monitoring chart display, but lacks an alarm function, and users can only find problems manually

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