Container technology is one of the latest industry trends, offering high elasticity and agility in #AI, #bigdata, and #edgecomputing. The popularity of container clusters has created a demand for simplified management, specifically for O&M. To avoid system interruptions caused by software and hardware faults, the following measures are used to analyze and prevent #containercluster faults:
- Detecting cluster component status
- Monitoring service entries
- Inspecting custom interfaces
Sadly, these measures fail to analyze the sub-health status or formulate fault diagnosis and execution policies, rendering them unuseful for certain faults.
Built for cloud-native applications, the #Linx Software's open source system Container Problem Detection System, or #CPDS, can monitor and identify faults and sub-health status of containers.
Specifically, the CPDS can detect and handle exceptions arising from #cluster base services, #OSs, and applications.
- The CPDS resolves cluster base service exceptions by using the systemd and initv processes to start and monitor policies for key services.
- For OSs, the CPDS invokes node agents to collect and report information about the network, system calls, I/Os, and especially drive Logical Volume Manager (LVM), and execute policies.
- Comprehensive analysis is performed on applications using cross-container-namespace agents in non-intrusive mode. This lets the CPDS monitor, analyze, and detect the application status, resource consumption, key system calls, and #I/O status in containers. For example, if the #CPU usage is high, the CPDS analyzes multiple parameters and metrics (such as irq and iowait) to locate the cause. What's more, the CPDS analyzes the network, system calls, and I/Os to inform decisions on troubleshooting.
The work process of the CPDS is as follows:
- The information collection module collects key data covering container cluster base services, system, and applications through the agents, and reports the data to the exception detection module.
- ?After preprocessing data, the exception detection module identifies potential exceptions from the data and uploads the results to the diagnosis module.
- ?The diagnosis module follows certain principles to analyze nodes and services, and displays the #diagnosis results using the user interaction module.
