1) "HA" pairs are commonly used but are not the only way to achieve redundancy. They have limitations around catastrophic failures and lack of scale out. 2) Alternative patterns like distributed load balancing and brokerless messaging can provide redundancy without single points of failure and allow for scale out. 3) Service distribution is presented as a superior approach that combines standard networking technologies to provide resilient, stateless and scale out services for OpenStack.

1. Redundancy Doesn't Always
Mean "HA" or "Cluster"
A cautionary tale against using hammers to solve all redundancy and resiliency problems ...
OpenStack Design Summit – Oct 2012
Randy Bias Dan Sneddon
@randybias @dxs
CTO, Cloudscaling Sr. Engineer, Cloudscaling
CCA - NoDerivs 3.0 Unported License - Usage OK, no modifications, full attribution*
* All unlicensed or borrowed works retain their original licenses 1
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2. Our Journey Today
1. “HA” pairs are not the only type of redundancy
2. Alternative redundancy patterns for HA
3. Redundancy patterns in Open Cloud System*
* Cloudscaling’s OpenStack-powered cloud operating system (“distribution”)
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3. What Do We Mean By “HA”?
We mean what most people mean ...
Two servers or network devices that look like one
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4. “HA HA”?
HA pairs come in a couple flavors
Active / Passive
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5. “HA HA”?
People like this flavor best, but it’s not always possible...
Active / Active
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6. “HA HA HA HA HA”??
Many people wish they could get it more like this ...
HA cluster aka ‘massive operational nightmare’
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7. Cluster<bleep>!
Imagine this was 4 or 6 nodes in the cluster
• 4 network tech.
• 7 NICs / node
• A million different ways
to break
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8. “HA” Pairs Are One Type of Redundancy
Herein lies the problem ...
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9. The Problem With “HA”-mmers
There are many, but these two matter most ...
• Catastrophic failures
• No scale out
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10. HA Pairs Have Binary Failures
Either working or dead, nothing in-between
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11. What is Scale-out?
A B
A B C D N
A B
Scale-up - Make boxes Scale-out - Make moar
bigger (usually an HA pair) boxes
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12. Scaling out is a mindset
Scaling up is like treating your servers as pets
bowzer.company.com web001.company.com
Servers *are* cattle
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13. HA Pair Failures* - 100% down
Hardware rarely fails, operators fail, software fails
Who Type Year Why Duration
Apple Switch 2005 Bug 2 hrs
Flexiscale SAN 2007 Ops Err 24 hrs
Vendio NAS 2008 Ops Err 8 hrs
UOL Brazil SAN 2011 Bug 72 hrs
Twitter Datacenter 2012 Bug+Ops 2 hrs
* This is a handful of examples as a baseline; I’m sure you can find many more
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14. “HA” Pairs Are an All-in Move
They better not fail ...
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15. Risk Reduction
Many small failure domains is usually better
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16. Big failure domains vs. small
Would you rather have the whole cloud down or just a
small bit for a short period of time?
Still a scale-up pattern ...
wouldn’t you rather scale-out?
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17. Pair vs. Scale-out Load Balancing
No scale-out
State Sync Shared-nothing Architecture
(100% loss) (20% loss)
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18. Pair vs. Scale-out Load Balancing
No scale-out
State Sync Shared-nothing Architecture
(100% loss) (20% loss)
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19. What’s Usually an “HA” Pair in OpenStack?
Everything ...
Service Endpoints Messaging System
(APIs) (RPC)
Worker Threads
Database
(e.g. Scheduler,
(MySQL)
Networking)
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20. What needs to be an HA pair?
Not much needs state synchronization
Service Endpoints Messaging System
(APIs) (RPC)
Worker Threads
Database
(e.g. Scheduler,
Networking) (MySQL)
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21. Fault Tolerance Methodologies
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22. Fault Tolerance in OCS
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23. Service Distribution
High Availability Without Compromise
Resilient Stateless Scale-out
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24. Service Distribution
Combines Standard Networking Technologies
router ospf
OSPF /etc/quagga/ospfd.conf ospf router-id 10.1.1.1
network 10.1.255.1 area 0.0.0.0
interface lo:2
Anycast /etc/quagga/zebra.conf description Pound listening address
ip address 10.1.255.1/32
ListenHTTP
Address 10.1.255.1
Port 8774
Load- xHTTP
Service
BackEnd
1
Balancing /etc/pound/pound.conf
End
Address 10.1.1.1
Port 8774
Proxy BackEnd
Address 10.1.1.2
Port 8774
End
End
End
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25. Resilient OpenStack
Horizontally Scalable, No Single Point Of Failure
Service Distribution ZeroMQ
Service Endpoints Messaging System
(APIs) (RPC)
Service Distribution MMR + HA
Worker Threads Database
(e.g. Scheduler,
Networking) (MySQL)
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26. Service Distribution Advantages
What Makes This a Superior Solution?
• True horizontal scalability with no centralized controller
• Services are always running, failover is nearly instant
• Reduced complexity, fewer idle resources
• No need for separate load balancers
Server Server Server Server Server Server Server
...
Failover vs. Distributed Services
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27. Perfect For Site Resiliency
Service Distribution Works With Multiple Sites
• Traditional HA pairs do not support cross-site resiliency
• Service Distribution fail across sites without DNS redirections
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28. Service Distribution in Action
Example: Distributed Load Balancing
1) OSPF
OSPF Router(s)
OSPF OSPF
advertisement advertisement
V
Quagga Quagga
HTTP Proxy HTTP Proxy
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29. Service Distribution in Action
Example: Distributed Load Balancing
1) OSPF
OSPF Router(s)
2) ECMP Per-flow
Load Balancing
OSPF OSPF
advertisement advertisement
Per-Flow
Load
3) Load-balancing V
Balancing
Quagga Quagga
HTTP Proxy
HTTP Proxy HTTP Proxy
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30. Service Distribution in Action
Example: Distributed Load Balancing
1) OSPF
OSPF Router(s)
2) ECMP Per-flow
Load Balancing
OSPF OSPF
advertisement advertisement
Per-Flow
Load
3) Load-balancing V
Balancing
Quagga Quagga
HTTP Proxy
HTTP Proxy HTTP Proxy
4) Unlimited #
of Back-End
Servers
Server Server Server Server
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31. Failure Resiliency
Client Client Client Client
1 2 3 4
1 2 3 4
Load Balancer/
Load Balancer/ Load Balancer/ Load Balancer/ Load Balancer/
Proxy
Proxy Proxy Proxy Proxy
10%
Server Server Server Server Server Load
Each
Server Server Server Server Server Server
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32. Failure Resiliency
Client Client Client Client
1 2 3 4
1 12 3 4
X
Load Balancer/
Load Balancer/ Load Balancer/ Load Balancer/ Load Balancer/
Proxy
Proxy Proxy Proxy Proxy
10%
Server Server Server Server Server Load
Each
Server Server Server Server Server Server
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33. Failure Resiliency
Client Client Client Client
1 2 3 4
1 2 3 4
Load Balancer/
Load Balancer/ Load Balancer/ Load Balancer/ Load Balancer/
Proxy
Proxy Proxy Proxy Proxy
10%
X
Server
Server
Server
Server
Server
Server
Server
Server
Server
Server
Increased
Server
Load
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34. OCS NAT Service
Example: Scale-out Network Address Translation
BGP Multiple ISP
providers
NAT
Service
Distribution
VMs
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35. Brokerless Messaging With ZeroMQ
Avoiding RabbitMQ’s Single Point Of Failure
Nova-Compute
Single Point
Of Failure
RabbitMQ
Broker
Nova-Scheduler Nova-API
RabbitMQ
(Brokered)
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36. Brokerless Messaging With ZeroMQ
Avoiding RabbitMQ’s Single Point Of Failure
Nova-Compute Nova-Compute
Single Point
Of Failure
RabbitMQ
Broker
Nova-Scheduler Nova-API Nova-Scheduler Nova-API
RabbitMQ vs. ZeroMQ
(Brokered) (Peer To Peer)
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37. What did we learn today?
1. HA-mmers are for nails
2. Scale-out rules for redundancy
3. Design-for-failure is a mentality, not a pair
4. Resiliency over redundancy
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38. Q&A
Randy Bias Dan Sneddon
@randybias @dxs
CTO, Cloudscaling Sr. Engineer, Cloudscaling
OCS 2.0
Public Cloud Benefits | Private Cloud Control | Open Cloud Economics
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