- 1 Distributed Monitoring
- 1.1 Overview
- 1.2 Architecture
- 1.3 Use Cases
- 1.4 Implementation
- 1.5 References
- 1.6 Who is contributing to this guide?
Monitoring and its application are becoming key factor for service lifecycle management of various systems such as NFV and cloud native platform. Distributed monitoring approach is one of the framework which enables flexible and scalable monitoring that can work with current OpenStack telemetry and monitoring framework. In this documantation, you can find the architecture and how to implement this framework to your environment.
Who will be interested in ?
- For infrastracture operators who want to collect detailed data in short intarval in their compute nodes.
- For NFV operators who want to know abnormal behaivers of Virtual Network Functions.
In this architecture includes several functions for monitoring, collector, in-memory database, analysis engine and notification. Below picture shows the architecture of distributed monitoring. Each compute node has it own monitoring function following this architecture.
- Poller/Notification process collect data from guest OSs and host OS using SNMP protocol, libvirt API, OpenStack API, etc.
- Collector format data suitable for in-memory database and insert these data into database.
- Analytics Engine analyzes data on in-memory database, you can use several analytics engine like machine learning libraries. And also you can use evaluator that function is threshold monitoring directly.
- Transmitter send analytics results and alarms that are caught on threshold to Operation Support System, OpenStack API and Orchestrator.
short interval, scalable, mq非依存
- Micro Burst Traffic
- Memory Leak
- Abnormal behaviour of software/hardware
For distributed monitoring, some open source softwares are useful. ...
collectd is one of the powerful collecting tools for timebase data... collectd has also several plugins and you can use threshold ,notification and python plugin.
redis is light in-memory database...
In this page, scikit-learn is recommended as light weight machine learning library for analytics engine.
In computing nodes, controller nods and every nodes that you want to monitor, you can setup following example. In this example, ubuntu16.04 is selected as each node's OS.
- set up OpenStack environment using DevStack or manual installation
- install collectd, redis and some other python library
- get demo code of DMA
# apt install collectd redis python-pip python-dev
<Plugin "write_redis"> <Node "dma"> Host "localhost" Port "6379" Timeout 1000 </Node> </Plugin> }
#save 300 10 #save 60 10000