Intelligent elastic telescoping method based on container cluster

Abstract

The invention discloses an intelligent elastic telescoping method based on a container cluster. The method comprises the following steps: creating a container cluster, and setting a threshold value ofelastic expansion, the threshold value being the maximum CPU usage rate, the maximum memory usage rate, the maximum disk IO, the maximum network throughput, the longest request response time and themaximum request rate of a target time period when a container runs; judging whether the containers in the container cluster are started or not, and executing corresponding operation according to a judgment result; after all the containers are started, setting a unified access point; enabling the monitoring module to monitor the running state of each host; sending the data acquired at regular timeto a monitoring server; enabling the monitoring module on each host to be responsible for sampling, counting and processing the real-time and historical CPU usage rate, memory usage rate, disk IO, network throughput, request response time, request rate and the like of the host and the container, the method can be flexibly configured and created, the resource occupation rate is low, and the telescoping method is more flexible and efficient.

Description

technical field [0001] The invention relates to the field of cloud computing, in particular to an intelligent elastic scaling method based on container clusters. Background technique [0002] Nowadays, the scale of enterprise IT system clusters is getting larger and larger, and various computing systems, storage systems, and application systems are built one after another with the rapid development of business. But at the same time, there are also problems, such as cumbersome deployment and maintenance, long launch cycle of new systems, and low overall cluster utilization. [0003] Since most cloud environments now use virtual machines as the basic unit, it takes a few minutes for the virtual machine to take effect from startup to deployment. In some scenarios, when the expanded cluster has just taken effect or has not yet taken effect, the workload has returned to normal. If the level is low, the expanded virtual machines are in a relatively idle state, so the expanded vir...

AI Technical Summary

Problems solved by technology

But at the same time, there are also problems, such as cumbersome deployment and maintenance, long launch cycle of new systems, and low overall utilization of clusters, etc.

[0003] Since most cloud environments now use virtual machines as the basic unit, it takes a few minutes for the virtual machine to take effect from s...

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