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Distributed Monitoring

Revision as of 12:56, 27 September 2017 by S1061123 (talk | contribs)

Distributed Monitoring


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
    • You can collect several data in short interval like 0.1 sec. Because collect agent doesn't have to collect data from huge amount of computing nodes and VMs in this architecture. That means the load of agent become lower than centralized monitoring architecture.
  • Scalable
    • This architecture has high scalability. Because each compute node has it own monitoring function, so you don't have to caluculate specs of nodes for monitoring.
  • Fast detection
    • In some case, MQ is bottlneck of performances. But any MQ isn't used in this architecture, because monitoring process is closed in compute node.

Use Cases

  • Memory Leak
    • First use case is memory leak detection, not only for compute node, also virtual machine running in compute node. In case of out-of-memory (OOM), a corresponding node could be out of control suddenly, hence cloud administrator needs to identify such condition before its uncontrollable state. Distributed monitoring can retrieve memory usage in short interval and identify the memory leak using machine learning by scikit-learn.
  • Micro Burst Traffic
    • Virtual machine's network statistics are very important for network operation, especially Network Function Virtualization (NFV) use-cases. Operator is watching network function (VNF) always to keep the network healthy. Unexpectedly network goes to trouble due to 'micro burst', i.e. massive traffic in very short duration, and it is hard to identify because the duration is very short than monitoring interval. Distributed monitoring enables to monitor network stats in very short interval (e.g. 0.1sec) and identify the target node.
  • Abnormal behaviour of software/hardware
    • Distributed monitoring enables to monitor various parameters without to communicate to controller node, with low latency, hence it could be utilized to identify abnormal state of virtual machine and hypervisor.
  • ...


For distributed monitoring, some open source software are useful. ... You can learn an example of how to implement distributed monitoring with open source software.



collectd is a daemon which collects system and application performance metrics periodically and provides mechanisms to store the values in a variety of ways. You can use collectd as Poller/Notification to gather metrics and Collector to store the metrics in Redis. You can implement Analytics Engine and Transmitter as collectd's plugins.

collectd's official page


Redis is an in-memory data structure store, used as a database, cache and message broker.

Redis's official page


scikit-learn is simple and efficient tools for data mining and data analysis. You can use scikit-learn as light weight machine learning library for Analytics Engine.

scikit-learn's official page


(to be edited) 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.

  1. set up OpenStack environment using DevStack or manual installation
  2. install collectd, redis and some other python library
  3. get demo code of DMA


You can implement distributed monitoring by setting collectd and making collectd's plugins. See distributed monitoring github repository if you want to see an example .


collectd has plugins to collect standard metrics such as cpu/memory/disk/network utiliziation. See collectd's official page.


(to be edited)

<Plugin "write_redis">
  <Node "dma">
    Host "localhost"
    Port "6379"
    Timeout 1000
Analytics Engine

Make analytics plugin with python and scikit learn. (to be edited)

<Plugin python>
  ModulePath "/opt/dma/lib"
  LogTraces true
  Interactive false
  Import "analysis"

  <Module "analysis">


Make transimit plugin with python. (to be edited)

<Plugin "threshold">
  <Host "localhost">
    <Plugin "dma">
      <Type "gauge">
        WarningMax 0
        Hits 3

<Plugin python>
   ModulePath "/opt/dma/lib"
     LogTraces true
     Interactive false
     Import "write_openstack"

     <Module "write_openstack">


We recommend that you disable saving data of Redis DB on disk.

#save 900 1
#save 300 10
#save 60 10000
save ""


Who is contributing to this guide?