openstack-manuals/doc/ops-guide/source/ops-monitoring.rst

Monitoring

There are two types of monitoring: watching for problems and watching usage trends. The former ensures that all services are up and running, creating a functional cloud. The latter involves monitoring resource usage over time in order to make informed decisions about potential bottlenecks and upgrades.

Process Monitoring

A basic type of alert monitoring is to simply check and see whether a required process is running. For example, ensure that the nova-api service is running on the cloud controller:

# ps aux | grep nova-api
nova 12786 0.0 0.0 37952 1312 ? Ss Feb11 0:00 su -s /bin/sh -c exec nova-api
--config-file=/etc/nova/nova.conf nova
nova 12787 0.0 0.1 135764 57400 ? S Feb11 0:01 /usr/bin/python
 /usr/bin/nova-api --config-file=/etc/nova/nova.conf
nova 12792 0.0 0.0 96052 22856 ? S Feb11 0:01 /usr/bin/python
/usr/bin/nova-api --config-file=/etc/nova/nova.conf
nova 12793 0.0 0.3 290688 115516 ? S Feb11 1:23 /usr/bin/python
/usr/bin/nova-api --config-file=/etc/nova/nova.conf
nova 12794 0.0 0.2 248636 77068 ? S Feb11 0:04 /usr/bin/python
/usr/bin/nova-api --config-file=/etc/nova/nova.conf
root 24121 0.0 0.0 11688 912 pts/5 S+ 13:07 0:00 grep nova-api

The OpenStack processes that should be monitored depend on the specific configuration of the environment, but can include:

Compute service (nova)

• nova-api
• nova-scheduler
• nova-conductor
• nova-novncproxy
• nova-cert
• nova-compute

Block Storage service (cinder)

• cinder-volume
• cinder-api
• cinder-scheduler

Networking service (neutron)

• neutron-api
• neutron-server
• neutron-openvswitch-agent
• neutron-dhcp-agent
• neutron-l3-agent
• neutron-metadata-agent

Image service (glance)

• glance-api
• glance-registry

Identity service (keystone)

The keystone processes can run standalone, but are often run within Apache as WSGI applications.

• keystone-public
• keystone-admin

Resource Alerting

Resource alerting provides notifications when one or more resources are critically low. While the monitoring thresholds should be tuned to your specific OpenStack environment, monitoring resource usage is not specific to OpenStack at all—any generic type of alert will work fine.

Some of the resources that you want to monitor include:

• Disk usage
• Server load
• Memory usage
• Network I/O
• Available vCPUs

Telemetry Service

The Telemetry service (ceilometer) collects metering and event data relating to OpenStack services. Data collected by the Telemetry service could be used for billing. Depending on deployment configuration, collected data may be accessible to users based on the deployment configuration. The Telemetry service provides a REST API documented at ceilometer V2 Web API. You can read more about the module in the OpenStack Administrator Guide or in the developer documentation.

OpenStack Specific Resources

Resources such as memory, disk, and CPU are generic resources that all servers (even non-OpenStack servers) have and are important to the overall health of the server. When dealing with OpenStack specifically, these resources are important for a second reason: ensuring that enough are available to launch instances. There are a few ways you can see OpenStack resource usage. The first is through the nova command:

# openstack usage list

This command displays a list of how many instances a tenant has running and some light usage statistics about the combined instances. This command is useful for a quick overview of your cloud, but it doesn't really get into a lot of details.

Next, the nova database contains three tables that store usage information.

The nova.quotas and nova.quota_usages tables store quota information. If a tenant's quota is different from the default quota settings, its quota is stored in the nova.quotas table. For example:

mysql> select project_id, resource, hard_limit from quotas;
+----------------------------------+-----------------------------+------------+
| project_id                       | resource                    | hard_limit |
+----------------------------------+-----------------------------+------------+
| 628df59f091142399e0689a2696f5baa | metadata_items              | 128        |
| 628df59f091142399e0689a2696f5baa | injected_file_content_bytes | 10240      |
| 628df59f091142399e0689a2696f5baa | injected_files              | 5          |
| 628df59f091142399e0689a2696f5baa | gigabytes                   | 1000       |
| 628df59f091142399e0689a2696f5baa | ram                         | 51200      |
| 628df59f091142399e0689a2696f5baa | floating_ips                | 10         |
| 628df59f091142399e0689a2696f5baa | instances                   | 10         |
| 628df59f091142399e0689a2696f5baa | volumes                     | 10         |
| 628df59f091142399e0689a2696f5baa | cores                       | 20         |
+----------------------------------+-----------------------------+------------+

The nova.quota_usages table keeps track of how many resources the tenant currently has in use:

mysql> select project_id, resource, in_use from quota_usages where project_id like '628%';
+----------------------------------+--------------+--------+
| project_id                       | resource     | in_use |
+----------------------------------+--------------+--------+
| 628df59f091142399e0689a2696f5baa | instances    | 1      |
| 628df59f091142399e0689a2696f5baa | ram          | 512    |
| 628df59f091142399e0689a2696f5baa | cores        | 1      |
| 628df59f091142399e0689a2696f5baa | floating_ips | 1      |
| 628df59f091142399e0689a2696f5baa | volumes      | 2      |
| 628df59f091142399e0689a2696f5baa | gigabytes    | 12     |
| 628df59f091142399e0689a2696f5baa | images       | 1      |
+----------------------------------+--------------+--------+

By comparing a tenant's hard limit with their current resource usage, you can see their usage percentage. For example, if this tenant is using 1 floating IP out of 10, then they are using 10 percent of their floating IP quota. Rather than doing the calculation manually, you can use SQL or the scripting language of your choice and create a formatted report:

+----------------------------------+------------+-------------+---------------+
| some_tenant                                                                 |
+-----------------------------------+------------+------------+---------------+
| Resource                          | Used       | Limit      |               |
+-----------------------------------+------------+------------+---------------+
| cores                             | 1          | 20         |           5 % |
| floating_ips                      | 1          | 10         |          10 % |
| gigabytes                         | 12         | 1000       |           1 % |
| images                            | 1          | 4          |          25 % |
| injected_file_content_bytes       | 0          | 10240      |           0 % |
| injected_file_path_bytes          | 0          | 255        |           0 % |
| injected_files                    | 0          | 5          |           0 % |
| instances                         | 1          | 10         |          10 % |
| key_pairs                         | 0          | 100        |           0 % |
| metadata_items                    | 0          | 128        |           0 % |
| ram                               | 512        | 51200      |           1 % |
| reservation_expire                | 0          | 86400      |           0 % |
| security_group_rules              | 0          | 20         |           0 % |
| security_groups                   | 0          | 10         |           0 % |
| volumes                           | 2          | 10         |          20 % |
+-----------------------------------+------------+------------+---------------+

The preceding information was generated by using a custom script that can be found on GitHub.

Note

This script is specific to a certain OpenStack installation and must be modified to fit your environment. However, the logic should easily be transferable.

Intelligent Alerting

Intelligent alerting can be thought of as a form of continuous integration for operations. For example, you can easily check to see whether the Image service is up and running by ensuring that the glance-api and glance-registry processes are running or by seeing whether glace-api is responding on port 9292.

But how can you tell whether images are being successfully uploaded to the Image service? Maybe the disk that Image service is storing the images on is full or the S3 back end is down. You could naturally check this by doing a quick image upload:

#!/bin/bash
#
# assumes that reasonable credentials have been stored at
# /root/auth


. /root/openrc
wget http://download.cirros-cloud.net/0.3.4/cirros-0.3.4-x86_64-disk.img
openstack image create --name='cirros image' --public \
--container-format=bare --disk-format=qcow2 \
--file cirros-0.3.4-x86_64-disk.img

By taking this script and rolling it into an alert for your monitoring system (such as Nagios), you now have an automated way of ensuring that image uploads to the Image Catalog are working.

Note

You must remove the image after each test. Even better, test whether you can successfully delete an image from the Image service.

Intelligent alerting takes considerably more time to plan and implement than the other alerts described in this chapter. A good outline to implement intelligent alerting is:

• Review common actions in your cloud.
• Create ways to automatically test these actions.
• Roll these tests into an alerting system.

Some other examples for Intelligent Alerting include:

• Can instances launch and be destroyed?
• Can users be created?
• Can objects be stored and deleted?
• Can volumes be created and destroyed?

Trending

Trending can give you great insight into how your cloud is performing day to day. You can learn, for example, if a busy day was simply a rare occurrence or if you should start adding new compute nodes.

Trending takes a slightly different approach than alerting. While alerting is interested in a binary result (whether a check succeeds or fails), trending records the current state of something at a certain point in time. Once enough points in time have been recorded, you can see how the value has changed over time.

All of the alert types mentioned earlier can also be used for trend reporting. Some other trend examples include:

• The number of instances on each compute node
• The types of flavors in use
• The number of volumes in use
• The number of Object Storage requests each hour
• The number of nova-api requests each hour
• The I/O statistics of your storage services

As an example, recording nova-api usage can allow you to track the need to scale your cloud controller. By keeping an eye on nova-api requests, you can determine whether you need to spawn more nova-api processes or go as far as introducing an entirely new server to run nova-api. To get an approximate count of the requests, look for standard INFO messages in /var/log/nova/nova-api.log:

# grep INFO /var/log/nova/nova-api.log | wc

You can obtain further statistics by looking for the number of successful requests:

# grep " 200 " /var/log/nova/nova-api.log | wc

By running this command periodically and keeping a record of the result, you can create a trending report over time that shows whether your nova-api usage is increasing, decreasing, or keeping steady.

A tool such as collectd can be used to store this information. While collectd is out of the scope of this book, a good starting point would be to use collectd to store the result as a COUNTER data type. More information can be found in collectd's documentation.

Monitoring Tools

Nagios

Nagios is an open source monitoring service. It is capable of executing arbitrary commands to check the status of server and network services, remotely executing arbitrary commands directly on servers, and allowing servers to push notifications back in the form of passive monitoring. Nagios has been around since 1999. Although newer monitoring services are available, Nagios is a tried-and-true systems administration staple.

You can create automated alerts for critical processes by using Nagios and NRPE. For example, to ensure that the nova-compute process is running on Compute nodes, create an alert on your Nagios server:

define service {
    host_name c01.example.com
    check_command check_nrpe_1arg!check_nova-compute
    use generic-service
    notification_period 24x7
    contact_groups sysadmins
    service_description nova-compute
}

On the Compute node, create the following NRPE configuration:

command[check_nova-compute]=/usr/lib/nagios/plugins/check_procs -c 1: -a nova-compute

Nagios checks that at least one nova-compute service is running at all times.

For resource alerting, for example, monitor disk capacity on a compute node with Nagios, add the following to your Nagios configuration:

define service {
    host_name c01.example.com
    check_command check_nrpe!check_all_disks!20% 10%
    use generic-service
    contact_groups sysadmins
    service_description Disk
}

On the compute node, add the following to your NRPE configuration:

command[check_all_disks]=/usr/lib/nagios/plugins/check_disk -w $ARG1$ -c $ARG2$ -e

Nagios alerts you with a WARNING when any disk on the compute node is 80 percent full and CRITICAL when 90 percent is full.

StackTach

StackTach is a tool that collects and reports the notifications sent by nova. Notifications are essentially the same as logs but can be much more detailed. Nearly all OpenStack components are capable of generating notifications when significant events occur. Notifications are messages placed on the OpenStack queue (generally RabbitMQ) for consumption by downstream systems. An overview of notifications can be found at System Usage Data.

To enable nova to send notifications, add the following to the nova.conf configuration file:

notification_topics=monitor
notification_driver=messagingv2

Once nova is sending notifications, install and configure StackTach. StackTach works for queue consumption and pipeline processing are configured to read these notifications from RabbitMQ servers and store them in a database. Users can inquire on instances, requests, and servers by using the browser interface or command-line tool, Stacky. Since StackTach is relatively new and constantly changing, installation instructions quickly become outdated. Refer to the StackTach Git repository for instructions as well as a demostration video. Additional details on the latest developments can be discovered at the official page

Logstash

Logstash is a high performance indexing and search engine for logs. Logs from Jenkins test runs are sent to logstash where they are indexed and stored. Logstash facilitates reviewing logs from multiple sources in a single test run, searching for errors or particular events within a test run, and searching for log event trends across test runs.

There are four major layers in Logstash setup which are:

• Log Pusher
• Log Indexer
• ElasticSearch
• Kibana

Each layer scales horizontally. As the number of logs grows you can add more log pushers, more Logstash indexers, and more ElasticSearch nodes.

Logpusher is a pair of Python scripts that first listens to Jenkins build events, then converts them into Gearman jobs. Gearman provides a generic application framework to farm out work to other machines or processes that are better suited to do the work. It allows you to do work in parallel, to load balance processing, and to call functions between languages. Later, Logpusher performs Gearman jobs to push log files into logstash. Logstash indexer reads these log events, filters them to remove unwanted lines, collapse multiple events together, and parses useful information before shipping them to ElasticSearch for storage and indexing. Kibana is a logstash oriented web client for ElasticSearch.