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System Architecture

High Level Description

The following diagram summarizes ceilometer logical architecture:


As shown in the above diagram, there are 5 basic components to the system:

  1. A compute agent runs on each compute node and polls for resource utilization statistics. There may be other types of agents in the future, but for now we will focus on creating the compute agent.
  2. A central agent runs on a central management server to poll for resource utilization statistics for resources not tied to instances or compute nodes.
  3. A collector runs on one or more central management servers to monitor the message queues (for notifications and for metering data coming from the agent). Notification messages are processed and turned into metering messages and sent back out onto the message bus using the appropriate topic. Metering messages are written to the data store without modification.
  4. A data store is a database capable of handling concurrent writes (from one or more collector instances) and reads (from the API server).
  5. An API server runs on one or more central management servers to provide access to the data from the data store. See API Description for details.

These services communicate using the standard OpenStack messaging bus. Only the collector and API server have access to the data store.

Detailed Description


These details cover only the compute agent and collector, as well as their communication via the messaging bus. More work is needed before the data store and API server designs can be documented.


Although we have described a list of the metrics ceilometer should collect, we cannot predict all of the ways deployers will want to measure the resources their customers use. This means that ceilometer needs to be easy to extend and configure so it can be tuned for each installation. A plugin system based on setuptools entry points makes it easy to add new monitors in the collector or subagents for polling.

Each daemon provides basic essential services in a framework to be shared by the plugins, and the plugins do the specialized work. As a general rule, the plugins are asked to do as little work as possible. This makes them more efficient as greenlets, maximizes code reuse, and makes them simpler to implement.

Installing a plugin automatically activates it the next time the ceilometer daemon starts. A global configuration option can be used to disable installed plugins (for example, one or more of the “default” set of plugins provided as part of the ceilometer package).

Plugins may require configuration options, so when the plugin is loaded it is asked to add options to the global flags object, and the results are made available to the plugin before it is asked to do any work.

Rather than running and reporting errors or simply consuming cycles for no-ops, plugins may disable themselves at runtime based on configuration settings defined by other components (for example, the plugin for polling libvirt does not run if it sees that the system is configured using some other virtualization tool). The plugin is asked once at startup, after it has been loaded and given the configuration settings, if it should be enabled. Plugins should not define their own flags for enabling or disabling themselves.


Plugin self-deactivation is not implemented, yet.

Each plugin API is defined by the namespace and an abstract base class for the plugin instances. Plugins are not required to subclass from the API definition class, but it is encouraged as a way to discover API changes.


There is ongoing work to add a generic plugin system to Nova. If that is implemented as part of the common library, ceilometer may use it (or adapt it as necessary for our use). If it remains part of Nova for Folsom we should probably not depend on it because loading plugins is trivial with setuptools.


Metering data comes from two sources: through notifications built into the existing OpenStack components and by polling the infrastructure (such as via libvirt). Polling for compute resources is handled by an agent running on the compute node (where communication with the hypervisor is more efficient). The compute agent daemon is configured to run one or more pollster plugins using the ceilometer.poll.compute namespace. Polling for resources not tied to the compute node is handled by the central agent. The central agent daemon is configured to run one or more pollster plugins using the ceilometer.poll.central namespace.

The agents periodically asks each pollster for instances of Counter objects. The agent framework converts the Counters to metering messages, which it then signs and transmits on the metering message bus.

The pollster plugins do not communicate with the message bus directly, unless it is necessary to do so in order to collect the information for which they are polling.

All polling happens with the same frequency, controlled by a global setting for the agent.

Handling Notifications

The heart of the system is the collector, which monitors the message bus for data being provided by the pollsters via the agent as well as notification messages from other OpenStack components such as nova, glance, quantum, and swift.

The collector loads one or more listener plugins, using the namespace ceilometer.collector. Each plugin can listen to any topics, but by default it will listen to

The plugin provides a method to list the event types it wants and a callback for processing incoming messages. The registered name of the callback is used to enable or disable it using the global configuration option of the collector daemon. The incoming messages are filtered based on their event type value before being passed to the callback so the plugin only receives events it has expressed an interest in seeing. For example, a callback asking for compute.instance.create.end events under ceilometer.collector.compute would be invoked for those notification events on the nova exchange using the topic.

The listener plugin returns an iterable with zero or more Counter instances based on the data in the incoming message. The collector framework code converts the Counter instances to metering messages and publishes them on the metering message bus. Although ceilomter includes a default storage solution to work with the API service, by republishing on the metering message bus we can support installations that want to handle their own data storage.

Handling Metering Messages

The listener for metering messages also runs in the collector daemon. It validates the incoming data and (if the signature is valid) then writes the messages to the data store.


Because this listener uses openstack.common.rpc instead of notifications, it is implemented directly in the collector code instead of as a plugin.

Metering messages are signed using the hmac module in Python’s standard library. A shared secret value can be provided in the ceilometer configuration settings. The messages are signed by feeding the message key names and values into the signature generator in sorted order. Non-string values are converted to unicode and then encoded as UTF-8. The message signature is included in the message for verification by the collector, and stored in the database for future verification by consumers who access the data via the API.


Ceilomter uses openstack.common.rpc to cast messages from the agent to the collector.

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