ceilometer

1. Physical Server Configuration 1.1 installationReference http://blog.csdn.net/qq_21398167/article/details/470197511.2 ConfigurationTurn off SELinux and firewalls# Setenforce 0 # Vi/etc/sysconfig/selinux modified to: SELINUX=disabled2. Testing in CeilometerAdd a meter that already exists2.1 New meter in Pipline.yamlAdd a Memory Meter,/etc/ceilometer/pipline.yaml-meters:"hardware.memory.*"resources:-snmp:// 172.16.1.212Sinks:-Meter_sink2.2 Restart

OpenStack Ceilometer use MongoDB to solve excessive disk space problem Background: Ceilometer uses MongoDB as a database, constantly sampling, resulting in the volume of data expansion, excessive disk space consumption. Knowledge background 1. database file type 1.1. Journal log file Unlike some traditional databases, MongoDB log files are used only to recover memory data that has not yet synchronized

, high data volume will be delayed or shutdown, not recommended to use Based on the alarm list (current enable alarm) 1.1.3 Partitionedalarmservice (Distributed calibration Service)PartitionedalarmserviceIt implements a set of collaboration protocols (Partitioncoordinator) between multiple evaluator processes through RPC, enabling the ability to continuously increase the processing power of alarm service through horizontal scaling, enabling a simple load balancing and high availabi

Ceilometer-alarm Common command line Reference: http://blog.csdn.net/violet_echo_0908/article/details/52243199 http://blog.csdn.net/hackerain/article/details/38172941 1 querying a resource for measurement Ceilometerstatistics-q resource_id= Ceilometerstatistics-q Resource_id=640a2be3-632b-4c2b-98f3-75b7f5877d4d-mcpu_util Ceilometeralarm-list 2 Create a alarm Ceilometeralarm-threshold-create--namecache--description ' instancerunning hot '--meter-n

Liberty version of Ceilometer table structure analysis 1 Overview Database: MongoDB Included tables: Alarm: Alarm Table Alarm_history: Alarm History table Dummydata:. Event: Events Table Meter: Table of measurement items Project: Item table Resource: Resource table statistics21600:21600 seconds (6 hours) of aggregate results table statistics300:300 seconds (5 minutes) of aggregate results table statistics3600:3600 seconds (1 hours) of aggregate resu

Creating a ceilometer alarm requires 4 DB operations:api/controllers/v2/alarms.py1. Every time Is_over_quota needs to query all the alarms of the User/project2. Determine if alarm name already exists in DB, and you need to query all alarms of the User/project# Make sure alarms is unique by name per project.Alarms = List (Conn.get_alarms (Name=data.name,PROJECT=DATA.PROJECT_ID))3. Add Alarm LineAlarm = conn.create_alarm (alarm_in)4. Add a Alarm history

The map_reduce mechanism of ceilometer, map_reduceMap/Reduce is an aggregation tool. For example, SQL, mongodb group (by), countdistinct, and so on are all aggregate commands.Map/Reduce is actually a software framework for implementing the idea of distributed computing. That is, you follow the specifications of this framework. Writing upper-Layer Code can implement your distributed computing and aggregate all the computing results to get a simple resu

piece of data, a reduce method, how to merge each piece of data. The framework sorts the output of the map operation and then inputs the results to the reduce task.Specific reference diagram:Summarize:The idea of Map/reduce is very simple, in other words, any language can be achieved. Google's map/reduce is famous not because the idea is ingenious, but because it sums up the distributed computing in a very simple way.Any distributed computing, the most core tasks are: 1, Task Division 2, Data

"} file {path = = ['/var/log/keystone/keystone-manage.log '] tags = [' keystone ', ' keys Tonefmt '] type = "Keystone"} file {path = = ['/var/log/glance/api.log '] tags = [' glance ', ' oslofmt ' ] Type = "glance-API "} file {path = = ['/var/log/glance/registry.log '] tags = [' glance ', ' oslofmt '] type =" Glance-r Egistry "} file {path = = ['/var/log/glance/scrubber.log '] tags = [' glance ', ' oslofmt '] type =" Glan Ce-scrubber "} file {path = = ['/var/log/

;$: fuel_settings ['Auto _ assign_floating_ip '], mysql_root_password => $ mysql_hash [root_password], admin_email => $ access_hash [email], admin_user => $ access_hash [user], admin_password => $ access_hash [Password], keystone_db_password => $ keystone_hash [db_password], keystone_admin_token => $ keysto Ne_hash [admin_token], Token => $ access_hash [Tenant], Token => $ glance_hash [db_password], Token => $ glance_hash [user_password], glance_backend => $ glance_backend, authorization => $ gl

Introduction to OpenStack Components Parsing (Advanced)Learning Goals: Mastering the architecture and functionality of more components The contents of this note are: Ceilmeter Component Parsing Heat Component Parsing Trove Component Parsing Sahara Component Parsing Ironic component parsing 1. Ceilometer Component ParsingAlso known as OpenStack Telemetry ( remote measurement data collection ), OpenStack is a