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[at scale]

OpenStack
Benchmarking
Boris Pavlovic
Mirantis, 2013
Agenda
● Benchmarking OpenStack at scale
○ What? Why? How?

● Rally
○ What is Rally?
○ Vision
○ Examples and results
Benchmarking OpenStack

● How to ensure that OpenStack works at scale?
● How to detect performance issues quickly and
improve OpenStack scalability?
A straightforward way to benchmark OpenStack
● Generate load from concurrent users
● Capture key metrics--avg/max time, failure rate
○ VM provisioning

○ Floating IP allocation
○ Snapshot creation
● Verify that the cloud works fine
...
● PROFIT!!!
A straightforward way to benchmark OpenStack
● Generate load from concurrent users
● Capture key metrics--avg/max time, failure rate
○ VM provisioning

○ Floating IP allocation
○ Snapshot creation
● Verify that the cloud works fine
...
● PROFIT!!!
… but what if it breaks apart?
Incorrect deployment setup?
Non-optimal hardware?
Bug in the code?
RTFM

Did you take enough time to
educate yourself?
;)
Really?
Read the docs… (after an hour)
There should be an
Improve OS cloud performance and scalability
● 3 common approaches:
○ Use better hardware

○ Deploy better
○ Make the code better
Improve OS cloud performance and scalability
● 3 common approaches:
○ Use better hardware

○ Deploy better
○ Make the code better
● But we need to know data points
○ Which part of the code is a bottleneck?
○ What hardware limits are hit, if any?
○ How deployment topology influences
performance?
Shine a light in the darkness

RALLY
What is Rally?
● Rally is a community-based project that allows
OpenStack developers and operators to get
relevant and repeatable benchmarking data of
how their cloud operates at scale.
● Wiki https://wiki.openstack.org/wiki/Rally
Relevant to both devs and operators
● Different types of user-defined workloads
○ For developers: synthetic tests, stress tests
○ For operators: real-life cloud usage patterns

● Flexible reporting
○ For developers: low-level profiling data, bottlenecks
○ For operators: high-level data about cloud
performance, highlights of bottlenecks within their
use case
How Rally works
RALLY
Run
specified
scenarios

Deploy
OpenStack
cloud

Deploy engines

Server Providers

DevStack

Virsh
OpenStack

Fuel
LXC
Dummy
Amazon

…

…

Parameters
● Number of
users
● Number of
tenants
● Concurrency
● Type of
workload
● Duration

Get results

Get results
● Execution
time
breakdown
● Failure rates
● Graphics
● Profiling data
Benchmarking scenarios
Data for Developers
- Low-level profiling
- Tomograph results
- Graphs

Synthetic workloads
Workload 1

OpenStack
cloud

Results

Workload 2

Workload 3
Real-life workloads

Data for Stakeholders
- Historical data
- SLAs
- Bottlenecks
Synthetic tests for developers
● Put stress test on various OpenStack components
○
○
○
○

Large number of provisioned VMs per second
Large number of provisioned volumes per second
Large number of uploaded images per second
Large amount of active resources (VMs/images/volumes)

● Expose bottlenecks and uncover design issues in
OpenStack

● Create a golden standard for everyone in the
community to validate against
How did we deploy OpenStack?

●
●
●
●
●

Using Fuel
On real hardware
3 physical controllers
500+ physical compute nodes
In HA deployment mode with Galera,
HAProxy, Corosync, Pacemaker
Large number of active VMs

Large numbers of active
VMs shouldn’t affect
provision of new VMs
Large number of concurrent users

Average time of
booting and deleting
VMs with different
numbers of concurrent
users
Profiling with Tomograph and Zipkin
Highlights:
●

Launch 3 VMs
○ 336 DB queries
○ 74 RPC calls

●

Delete 3 VMs under high load
○ 1 minute global DB lock
on quotas table
Why real workloads in addition to synthetic?
● Rationale
○
○
○

In the real world, scenarios are more complicated, than “boot-destroy”
immediately
Workloads rarely change--OpenStack and its topology/configuration
change often
Profiles are specific for businesses

● Expected outcome
○
○

Let companies specify their existing workload and benchmark cloud
according to this workload
Let companies share
What to benchmark

Provision VMs

1.

How long (on average)?

2.

How long (maximum)?

3.

Success rate?

Use VMs

Destroy VMs

How long (on average)?

How long (maximum)?

Success rate?
Detailed benchmark of each step

schedule

compute

network

glance

nova-api

nova-db

compute

network

nova-dd

Destroy VMs

nova-db

Use VMs

nova-api

Provision VMs

1s

2s

9s

4s

8s

2m

1s

2s

9s

4s

8s
Another workload representation
What it shows
● Areas of biggest
concern
● A baseline for all
future changes
(OpenStack version,
deployment
topology, Neutron
plugin)
What we ultimately want to achieve
● Provide a mechanism to
easily define workloads
● Let users benchmark their
cloud within specified
workload
● Provide historical data on
all applied optimizations to
see if they are heading to
better performance
Roadmap
● Greatly improve profiling capabilities to quickly
pinpoint problem location
● Extend workload definitions to support richer and
more realistic tests, combine workloads
● Support historical data and provide means of
comparison/analytics
● Better correlation between business KPIs and
reporting
Rally--OpenStack Benchmarking at Scale
Join Rally community
● It’s up to you to make Rally better
● Join our team:
○ Wiki: https://wiki.openstack.org/wiki/Rally
○ Project space: https://launchpad.net/rally
○ IRC chat: #openstack-rally on irc.freenode.net

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Rally--OpenStack Benchmarking at Scale

  • 2. Agenda ● Benchmarking OpenStack at scale ○ What? Why? How? ● Rally ○ What is Rally? ○ Vision ○ Examples and results
  • 3. Benchmarking OpenStack ● How to ensure that OpenStack works at scale? ● How to detect performance issues quickly and improve OpenStack scalability?
  • 4. A straightforward way to benchmark OpenStack ● Generate load from concurrent users ● Capture key metrics--avg/max time, failure rate ○ VM provisioning ○ Floating IP allocation ○ Snapshot creation ● Verify that the cloud works fine ... ● PROFIT!!!
  • 5. A straightforward way to benchmark OpenStack ● Generate load from concurrent users ● Capture key metrics--avg/max time, failure rate ○ VM provisioning ○ Floating IP allocation ○ Snapshot creation ● Verify that the cloud works fine ... ● PROFIT!!! … but what if it breaks apart?
  • 8. Bug in the code?
  • 9. RTFM Did you take enough time to educate yourself? ;)
  • 11. Read the docs… (after an hour)
  • 13. Improve OS cloud performance and scalability ● 3 common approaches: ○ Use better hardware ○ Deploy better ○ Make the code better
  • 14. Improve OS cloud performance and scalability ● 3 common approaches: ○ Use better hardware ○ Deploy better ○ Make the code better ● But we need to know data points ○ Which part of the code is a bottleneck? ○ What hardware limits are hit, if any? ○ How deployment topology influences performance?
  • 15. Shine a light in the darkness RALLY
  • 16. What is Rally? ● Rally is a community-based project that allows OpenStack developers and operators to get relevant and repeatable benchmarking data of how their cloud operates at scale. ● Wiki https://wiki.openstack.org/wiki/Rally
  • 17. Relevant to both devs and operators ● Different types of user-defined workloads ○ For developers: synthetic tests, stress tests ○ For operators: real-life cloud usage patterns ● Flexible reporting ○ For developers: low-level profiling data, bottlenecks ○ For operators: high-level data about cloud performance, highlights of bottlenecks within their use case
  • 18. How Rally works RALLY Run specified scenarios Deploy OpenStack cloud Deploy engines Server Providers DevStack Virsh OpenStack Fuel LXC Dummy Amazon … … Parameters ● Number of users ● Number of tenants ● Concurrency ● Type of workload ● Duration Get results Get results ● Execution time breakdown ● Failure rates ● Graphics ● Profiling data
  • 19. Benchmarking scenarios Data for Developers - Low-level profiling - Tomograph results - Graphs Synthetic workloads Workload 1 OpenStack cloud Results Workload 2 Workload 3 Real-life workloads Data for Stakeholders - Historical data - SLAs - Bottlenecks
  • 20. Synthetic tests for developers ● Put stress test on various OpenStack components ○ ○ ○ ○ Large number of provisioned VMs per second Large number of provisioned volumes per second Large number of uploaded images per second Large amount of active resources (VMs/images/volumes) ● Expose bottlenecks and uncover design issues in OpenStack ● Create a golden standard for everyone in the community to validate against
  • 21. How did we deploy OpenStack? ● ● ● ● ● Using Fuel On real hardware 3 physical controllers 500+ physical compute nodes In HA deployment mode with Galera, HAProxy, Corosync, Pacemaker
  • 22. Large number of active VMs Large numbers of active VMs shouldn’t affect provision of new VMs
  • 23. Large number of concurrent users Average time of booting and deleting VMs with different numbers of concurrent users
  • 24. Profiling with Tomograph and Zipkin Highlights: ● Launch 3 VMs ○ 336 DB queries ○ 74 RPC calls ● Delete 3 VMs under high load ○ 1 minute global DB lock on quotas table
  • 25. Why real workloads in addition to synthetic? ● Rationale ○ ○ ○ In the real world, scenarios are more complicated, than “boot-destroy” immediately Workloads rarely change--OpenStack and its topology/configuration change often Profiles are specific for businesses ● Expected outcome ○ ○ Let companies specify their existing workload and benchmark cloud according to this workload Let companies share
  • 26. What to benchmark Provision VMs 1. How long (on average)? 2. How long (maximum)? 3. Success rate? Use VMs Destroy VMs How long (on average)? How long (maximum)? Success rate?
  • 27. Detailed benchmark of each step schedule compute network glance nova-api nova-db compute network nova-dd Destroy VMs nova-db Use VMs nova-api Provision VMs 1s 2s 9s 4s 8s 2m 1s 2s 9s 4s 8s
  • 28. Another workload representation What it shows ● Areas of biggest concern ● A baseline for all future changes (OpenStack version, deployment topology, Neutron plugin)
  • 29. What we ultimately want to achieve ● Provide a mechanism to easily define workloads ● Let users benchmark their cloud within specified workload ● Provide historical data on all applied optimizations to see if they are heading to better performance
  • 30. Roadmap ● Greatly improve profiling capabilities to quickly pinpoint problem location ● Extend workload definitions to support richer and more realistic tests, combine workloads ● Support historical data and provide means of comparison/analytics ● Better correlation between business KPIs and reporting
  • 32. Join Rally community ● It’s up to you to make Rally better ● Join our team: ○ Wiki: https://wiki.openstack.org/wiki/Rally ○ Project space: https://launchpad.net/rally ○ IRC chat: #openstack-rally on irc.freenode.net