EfficientMetering

= Efficient Metering in OpenStack Blueprint =

Project Home Page: Ceilometer

Meetings : http://wiki.openstack.org/Meetings/MeteringAgenda

Uses cases

 * need a tool to collect per customer usage
 * need an API to query collected data from existing billing system
 * data needed per customer, with an hour level granularity, includes:
 * Compute - Nova:
 * instances (type, availability zone) - hourly usage
 * cpu - hourly usage
 * ram - hourly usage
 * nova volume block device (type, availability zone) - hourly usage
 * reserved
 * used
 * network (data in/out, availability zone) - hourly bytes + total bytes
 * differentiate between internal and external end-points
 * External floating IP - hourly bytes + total bytes
 * Storage - Swift
 * total data stored
 * data in/out - hourly bytes + total bytes
 * differentiate between internal and external end-points

Meters
A more current list of implemented meters is available at http://docs.openstack.org/developer/ceilometer/measurements.html

The following is a first list of meters that needs to be collected in order to allow billing systems to perform their tasks. This list must be expandable over time and each administrator must have the possibility to enable or disable each meter based on his local needs.

Other possible meters:
 * service handlers (load balancer, databases, queues...)
 * service usage

Note for network meters (n1-n4): the distinction between internal and external traffic requires that internal networks be explicitly listed in the agent configuration.

Note(dhellmann): That isn't going to scale to a real system where tenants may create their own networks. We should just collect the data for each network, and let the billing system decide on the rate at which to charge (possibly $0 for internal networks).

Storage

 * db is not directly accessible by any other mean than API
 * a process must collect messages from agent and store data
 * a process may validate meters against nova event database
 * a process may verify that messages were not lost
 * a process may verify that accounts states are in sync with keystone

Note: The instance_metadata field content is duplicated for each meter. For instance it will be duplicated for all c? fields. The storage optimization is to be dealt with in future versions of ceilometer.

Note: The storage may collapse records or it may be done by the API may collapse records as an optimisation to reduce the amount of information that is returned. For instance, if all fields from two consecutive c1 counter are equal and they are adjacent in time (i.e meter_datetime[second] - meter_datetime[first] == meter_duration[second] - meter_duration[second] ), then the first record can be removed because it is redundant.

Alternative gauge design
During the Folsom ODS session, an alternate design was discussed where events instead of recoding deltas, would record the absolute value of a gauge. That would require to extend the event to include the 'object id' (instance, network, volume) associated with the meter.

The delta model can be derived from the absolute model, and means it's resilient in the face of missing delta registration.

Agents

 * Agent on each nova compute node to accumulate and send meters for c1, c2, c3, c4, c5, n1, n2, n3, n4. The agent is likely to be pulling this information from libvirt.
 * c5 could get disk I/O stats with libvirt's virDomainBlockStats
 * n3 / n4 could use iptables accounting rules ? (for external traffic ?)
 * n1 / n2 could use libvirt's virDomainInterfaceStats ? (for all traffic ?)
 * Agent on each nova volume node to accumulate and send meters for v1, v2
 * Agent on each swift proxy to forward existing accounting data o1 and accumulate and send o2-o5

Note: nova network node need not accumulate and send meters for n5 because they can be pulled directly from the nova database ( see nova-manage floating list for instance )

Architecture
See also EfficientMetering/ArchitectureProposalV1


 * An agent runs on each OpenStack node ( Bare Metal machine ) and harvests the data localy
 * If a meter is available from the existing OpenStack component it should be used
 * A standalone ceilometer agent implements the meters that are not yet available from the existing OpenStack components
 * A storage daemon communicates with the agents to collect their data and aggregate them
 * The agents collecting data are authenticated to avoid pollution of the metering service
 * The data is sent from agents to the storage daemon via a trusted messaging system (RabbitMQ?)
 * The data / messages exchanged between agents and the storage daemon use a common messages format
 * The content of the storage is made available thru a REST API providing aggregation
 * The message queue is separate from other queues (such as the nova queue)
 * The messages in queue are signed and non repudiable (http://en.wikipedia.org/wiki/Non-repudiation)

Note(jking-6): The messaging format should use protocol buffers. JSON bytestrings take up too much bandwidth and time to parse.

Note: document some use case scenarios to really nail down the architecture. Who signals the metering service? The API service or nova, quantum, swift, glance, volume?

Note: ideally, all meters are available from the OpenStack component responsible for a given resource (for instance the disk I/O for an ephemeral disk is made available in nova). However, it is not realistic to assume it can always be the case. Standalone ceilometer agents runing on OpenStack nodes provide access to the meters when the OpenStack component don't. The meter implemented in ceilometer agents should always be contributed to the OpenStack component. This kind of incubation for each given meter ( first implemented in ceilometer agents and then in the OpenStack component ) is both practical for short term purposes and a sound long term practice that avoids forking code.

Messaging use cases
Instance creation
 * An instance is created, nova issues a message ( http://wiki.openstack.org/SystemUsageData )
 * The metering storage agent listens on the nova queue and picks up the creation message
 * The metering storage agent stores the creation event locally, with a timestamp
 * The metering storage daemon is notified by the agent that the instance has been created five minutes ago and aggregates this information in the tenant records

API

 * API design spec
 * API implementation documentation

Volume of data
A metering system will always generate massive amounts of data. In order to estimate the amounts that your cloud may generate, a Google spreadsheet has been proposed.

Contributing to Ceilometer
The developer documentation is starting to take shape within the source and is also published at http://ceilometer.readthedocs.org in a more friendly format.

The project team hangs out on Freenode in the #openstack-metering channel, feel free to drop by and stay as long as you want to discuss your future implementation. We use the OpenStack General Mailing List for our email discussions tagging the the subject with [metering].

If you wonder what you could contribute to ceilometer, here is a list of features that we are missing.

Roadmap
See EfficientMetering/RoadMap

Free Software Billing Systems
A list of the billing system implementations that could use the Metering system when it becomes available.


 * Dough https://github.com/lzyeval/dough [returns 404 as of 2015-03-06]
 * trystack.org billing https://github.com/trystack/dash_billing
 * nova-billing https://github.com/griddynamics/nova-billing

Related resources

 * Definition of a Storage Accounting Record http://www.ogf.org/Public_Comment_Docs/Documents/2012-02/EMI-StAR-OGF-info-doc-v2.pdf
 * UsageRecord format http://www.ogf.org/documents/GFD.98.pdf
 * Capturing exchanges https://github.com/rackspace/stacktach
 * Messages about system usage http://wiki.openstack.org/SystemUsageData
 * http://etherpad.openstack.org/EfficientMetering
 * Use https://github.com/stackforge
 * lzyeval codebase: [returns 404 as of 2015-03-06]
 * billing https://github.com/lzyeval/dough
 * metering https://github.com/lzyeval/kanyun
 * trystack.org codebase:
 * https://github.com/trystack/dash_billing
 * http://wiki.openstack.org/utilizationdata
 * Nova billing https://github.com/griddynamics/nova-billing
 * Swift
 * Retrieve Account Metadata http://docs.openstack.org/trunk/openstack-object-storage/developer/content/retrieve-account-metadata.html
 * swift middlewares examples :
 * https://github.com/spilgames/swprobe (https://lists.launchpad.net/openstack/msg07794.html)
 * https://github.com/pandemicsyn/swift-informant (https://lists.launchpad.net/openstack/msg07795.html)
 * April 2012 mailing list thread on billing https://lists.launchpad.net/openstack/msg10334.html
 * Virgo (scriptable agent for meter collection): https://github.com/racker/virgo
 * Contact Brandon Philips at Rackspace - brandon.philips@rackspace.com
 * Ovirt DWH http://www.ovirt.org/wiki/Ovirt_DWH and associated database schema http://gerrit.ovirt.org/gitweb?p=ovirt-dwh.git;a=blob;f=data-warehouse/historydbscripts_postgres/create_tables.sql;h=2e05299a2de1b79634e862e5f1811dda3f303a96;hb=0271e5205ad29109c2e2313e7f6fb900e76a757a#l377
 * Swift http://folsomdesignsummit2012.sched.org/event/d9135eabdd775432c74c3f1d32a325d3 and http://etherpad.openstack.org/FolsomSwiftStatsd
 * Collecting meters from libvirt https://github.com/ss7pro/rescnt
 * Doug Hellman sandbox https://github.com/dhellmann/metering-prototype/
 * Prototype ceilometer implementation http://github.com/woorea/ceilometer-java and discussion https://lists.launchpad.net/openstack/msg11410.html

Resources

 * A slide deck that Julien Danjou used to present Ceilometer in July 2012.

FAQ
Q: why reinvent the wheel ? XXXX already does it.

A: please mail about the tool you think does the work, unless it is listed below.
 * http://wiki.openstack.org/SystemUsageData for instance is specific to nova while the metering aims at aggregating all OpenStack components
 * collectd, munin etc. all have some pieces of the puzzle but do not have all of them and they are not designed with billing in mind and are not a good fit for this blueprint
 * Riemann -- http://aphyr.github.com/riemann/concepts.html I was able to get a basic dashboard up in an afternoon. Even if it's not a good fit for this project there are plenty of good ideas worth pilfering: protocol buffers, push-based dataflow graphs, extremely simple APIs (a stream processor is just a function that takes a single argument, an event message).