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Benchmarking your cloud performance with top 4
global public clouds
Boyan Krosnov
data://disrupted
2020
● Chief of Product & co-founder at StorPool
● 20+ years in ISPs, SDN, SDS
● IT Infrastructure with a focus on invention,
performance & efficiency
About me
https://www.linkedin.com/in/krosnov/
bk@storpool.com
About StorPool
● NVMe software-defined storage for VMs and containers
● Scale-out, HA, API-controlled
● Since 2011, in commercial production use since 2013
● Based in Sofia, Bulgaria
● Mostly virtual disks for KVM
● … and bare metal Linux hosts
● Also used with VMWare, Hyper-V, XenServer
● Integrations into OpenStack/Cinder, Kubernetes Persistent
Volumes, CloudStack, OpenNebula, OnApp
3
Why performance
● Better application performance -- e.g. time to load a page, time to
rebuild, time to execute specific query
● Happier customers in cloud and multi-tenant environments
● ROI, TCO - Lower cost per delivered resource (per VM) through
higher density
● Public cloud - win customers over from your competitors
● Private cloud - do more with less; win applications / workloads /
teams over from public cloud
1. Understanding performance
2. Benchmarks of public clouds
3. How to measure measure and optimize your own cloud
4. What's in a TCO
5. Conclusion
Agenda
Latency
ops
per
second
best service
6
Latency
ops
per
second
best service
lowest cost per
delivered resource
7
Latency
ops
per
second
best service
lowest cost per
delivered resource
only pain
8
Latency
ops
per
second
best service
lowest cost per
delivered resource
only pain
9
benchmarks
Benchmarks
Real load
?
?
1. Understanding performance
2. Benchmarks of public clouds
3. How to measure and optimize your own cloud
4. What's in a TCO
5. Conclusion
Agenda
* - ramdisk used to reduce usable RAM to 16 GB
VMs and block storage
Provider Instance name region
monthly cost
(with 12 month
commitment)
vCPUs RAM free -m
AWS Compute optimized: c5.2xlarge us-east-2 $245 8 16GB 15,437
Google Cloud General purpose: n2-8vcpu-16gb us-central1 $197 8 32GB 32,116*
Microsoft Azure
Compute optimized:
Standard_F8s_v2 - 8 vcpus, 16
GiB memory
East US 2 $235 8 16GB 15,962
Digital Ocean CPU Optimized Droplet: 16GB sfo2 $160 8 16GB 16,039
Katapult ROCK-24 London $120 8 24GB 23,458*
Storage volume
Size of volume
[GiB]
IOPS limit Monthly cost
AWS - EBS gp2 1024 3,072.00 $102
Google Cloud - SSD persistent disk 1T 1024 15,000.00 $174
Microsoft Azure - Premium SSD 1T 1024 3,500.00 $123
DigitalOcean - Block Storage 1T 1024 10,000.00 $102
Katapult Shared disk NVMe (StorPool-based) 1024 unlimited $154
● Storage heavy, a little CPU
○ FIO, rsync
● Storage & CPU
○ pgbench, sysbench
● CPU, RAM*
○ coremark
● Network*
* - future additions to our suite
Tools used
Results - FIO Storage type
FIO rand r/w
QD1 latency
[ms]
FIO QD1
random r/w
IOPS
FIO QD64
random r/w
IOPS
Katapult 1T ($153) StorPool-based 0.10 ms 10,101 IOPS 113,447 IOPS
AWS EBS gp2 1T ($102) 0.36 ms 2,762 IOPS 3,123 IOPS
Google Cloud SSD Persistent Disk 1T
($174)
0.72 ms 1,386 IOPS 15,436 IOPS
Azure Premium SSD 1T ($124) 8.18 ms 122 IOPS 5,100 IOPS
DO Block Storage 1T ($102) 3.34 ms 299 IOPS 1,044 IOPS
Results - rsync
storage type seconds to re-sync
Katapult 1T ($153)
StorPool-based
85
AWS EBS gp2 1T ($102) 176
Google Cloud SSD Persistent
Disk 1T ($174)
281
Azure Premium SSD 1T ($124) 431
DO Block Storage 1T ($102) 1,303
Results - pgbench
Results - pgbench
Unfit for databases!
Results - pgbench
Results - pgbench
2.3x transactions
with same CPU,
RAM / same cost
Results - pgbench
2.5-3x lower latency at
fixed load
1. Understanding performance
2. Benchmarks of public clouds
3. How to measure and optimize your own cloud
4. What's in a TCO
5. Conclusion
Agenda
● Design benchmarks which reflect your use-case and application
● Measure what matters. Examples:
○ developer productivity - simple SQL database for up to X users, so no
need to pay for complexity of clusters; runs CI/tests in half the time
○ Efficiency - $ per user, $ per features
● If you can't measure what matters directly, find good proxies. Example:
○ "I can't run my entire stack as a benchmark, but I know it consists of
a load balancer and a transaction-heavy database, so I'll use a load
balancer and a DB benchmark"
Benchmarks
Storage benchmarks
Beware: lots of snake oil out there!
● performance numbers from hardware configurations totally
unlike what you’d use in production
● synthetic tests with high iodepth - 10 nodes, 10 workloads *
iodepth 256 each. (because why not)
● testing with ramdisk backend
● synthetic workloads don't approximate real world
● Previous version of our tools and methodology:
○ https://storpool.com/storage-performance-and-resilience-
testing
● We'll be releasing updated tools and method with the write-up
in the next month
○ coremark, fio, rsync, pgbench, sysbench
● Until then drop us an email at info@storpool.com
Benchmarks
1. Your existing hardware can give you more
a. See Venko's talk on KVM optimization (tomorrow 11am)
b. fast networking (OVS-DPDK), fast storage (StorPool)
2. If you are building a new cloud - optimize for your use-case
a. per-rack power limit
b. per-core performance, per-core memory, per-core storage
c. per-core cost
Hardware
Hardware optimization example: Xeon scalable selection
● Hardware
● Host OS and hypervisor (KVM)
● Virtual networking, service mesh
● Storage
Optimization areas
1. Understanding performance
2. Benchmarks of public clouds
3. How to measure and optimize your own cloud
4. What's in a TCO
5. Conclusion
Agenda
● Define minimum service level
● When comparing options use TCO tool (large spreadsheet) to find
lowest cost per delivered unit of infrastructure (fixed-size
VM/container with associated storage and networking)
● 100s of parameters
● Usable for both public and private scenarios
TCO approach
https://storpool.com/public-and-private-cloud-roi-calculator
TCO example:
1. Datacenter
- power, cooling, max power per rack, remote hands
2. Compute
- servers, CPUs, RAM, minimum core performance, cloud
orchestration, management cost
3. Storage
- storage servers, drives, software, management cost
4. Network
- virtual network, CPU/RAM allocation, software, management
cost
- public/wide area network, IP transit cost
What to include
1. Understanding performance
2. Benchmarks of public clouds
3. How to measure and optimize your own cloud
4. What's in a TCO
5. Conclusion
Agenda
1. You can't judge a VM by its vCPUs and vRAM
2. Measure what matters to you
3. If you are a public or private cloud 2x,3x, higher application
performance (per $ !) than hyperscalers is within reach. Half price
for the same workload!
4. On your next project work with partners who understand
performance. You can gain a lot!
Conclusions
Questions?
StorPool
Storage
@storpool StorPool
Storage
StorPool
Storage
StorPool
Storage
StorPool
Storage
Follow StorPool Online
Talk to us:
The virtual booth at Data://Disrupted
info@storpool.com
https://storpool.com
Thank you!

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Benchmarking your cloud performance with top 4 global public clouds

  • 1. Benchmarking your cloud performance with top 4 global public clouds Boyan Krosnov data://disrupted 2020
  • 2. ● Chief of Product & co-founder at StorPool ● 20+ years in ISPs, SDN, SDS ● IT Infrastructure with a focus on invention, performance & efficiency About me https://www.linkedin.com/in/krosnov/ bk@storpool.com
  • 3. About StorPool ● NVMe software-defined storage for VMs and containers ● Scale-out, HA, API-controlled ● Since 2011, in commercial production use since 2013 ● Based in Sofia, Bulgaria ● Mostly virtual disks for KVM ● … and bare metal Linux hosts ● Also used with VMWare, Hyper-V, XenServer ● Integrations into OpenStack/Cinder, Kubernetes Persistent Volumes, CloudStack, OpenNebula, OnApp 3
  • 4. Why performance ● Better application performance -- e.g. time to load a page, time to rebuild, time to execute specific query ● Happier customers in cloud and multi-tenant environments ● ROI, TCO - Lower cost per delivered resource (per VM) through higher density ● Public cloud - win customers over from your competitors ● Private cloud - do more with less; win applications / workloads / teams over from public cloud
  • 5. 1. Understanding performance 2. Benchmarks of public clouds 3. How to measure measure and optimize your own cloud 4. What's in a TCO 5. Conclusion Agenda
  • 8. Latency ops per second best service lowest cost per delivered resource only pain 8
  • 9. Latency ops per second best service lowest cost per delivered resource only pain 9 benchmarks
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  • 14. 1. Understanding performance 2. Benchmarks of public clouds 3. How to measure and optimize your own cloud 4. What's in a TCO 5. Conclusion Agenda
  • 15. * - ramdisk used to reduce usable RAM to 16 GB VMs and block storage Provider Instance name region monthly cost (with 12 month commitment) vCPUs RAM free -m AWS Compute optimized: c5.2xlarge us-east-2 $245 8 16GB 15,437 Google Cloud General purpose: n2-8vcpu-16gb us-central1 $197 8 32GB 32,116* Microsoft Azure Compute optimized: Standard_F8s_v2 - 8 vcpus, 16 GiB memory East US 2 $235 8 16GB 15,962 Digital Ocean CPU Optimized Droplet: 16GB sfo2 $160 8 16GB 16,039 Katapult ROCK-24 London $120 8 24GB 23,458* Storage volume Size of volume [GiB] IOPS limit Monthly cost AWS - EBS gp2 1024 3,072.00 $102 Google Cloud - SSD persistent disk 1T 1024 15,000.00 $174 Microsoft Azure - Premium SSD 1T 1024 3,500.00 $123 DigitalOcean - Block Storage 1T 1024 10,000.00 $102 Katapult Shared disk NVMe (StorPool-based) 1024 unlimited $154
  • 16. ● Storage heavy, a little CPU ○ FIO, rsync ● Storage & CPU ○ pgbench, sysbench ● CPU, RAM* ○ coremark ● Network* * - future additions to our suite Tools used
  • 17. Results - FIO Storage type FIO rand r/w QD1 latency [ms] FIO QD1 random r/w IOPS FIO QD64 random r/w IOPS Katapult 1T ($153) StorPool-based 0.10 ms 10,101 IOPS 113,447 IOPS AWS EBS gp2 1T ($102) 0.36 ms 2,762 IOPS 3,123 IOPS Google Cloud SSD Persistent Disk 1T ($174) 0.72 ms 1,386 IOPS 15,436 IOPS Azure Premium SSD 1T ($124) 8.18 ms 122 IOPS 5,100 IOPS DO Block Storage 1T ($102) 3.34 ms 299 IOPS 1,044 IOPS
  • 18. Results - rsync storage type seconds to re-sync Katapult 1T ($153) StorPool-based 85 AWS EBS gp2 1T ($102) 176 Google Cloud SSD Persistent Disk 1T ($174) 281 Azure Premium SSD 1T ($124) 431 DO Block Storage 1T ($102) 1,303
  • 20. Results - pgbench Unfit for databases!
  • 22. Results - pgbench 2.3x transactions with same CPU, RAM / same cost
  • 23. Results - pgbench 2.5-3x lower latency at fixed load
  • 24. 1. Understanding performance 2. Benchmarks of public clouds 3. How to measure and optimize your own cloud 4. What's in a TCO 5. Conclusion Agenda
  • 25. ● Design benchmarks which reflect your use-case and application ● Measure what matters. Examples: ○ developer productivity - simple SQL database for up to X users, so no need to pay for complexity of clusters; runs CI/tests in half the time ○ Efficiency - $ per user, $ per features ● If you can't measure what matters directly, find good proxies. Example: ○ "I can't run my entire stack as a benchmark, but I know it consists of a load balancer and a transaction-heavy database, so I'll use a load balancer and a DB benchmark" Benchmarks
  • 26. Storage benchmarks Beware: lots of snake oil out there! ● performance numbers from hardware configurations totally unlike what you’d use in production ● synthetic tests with high iodepth - 10 nodes, 10 workloads * iodepth 256 each. (because why not) ● testing with ramdisk backend ● synthetic workloads don't approximate real world
  • 27. ● Previous version of our tools and methodology: ○ https://storpool.com/storage-performance-and-resilience- testing ● We'll be releasing updated tools and method with the write-up in the next month ○ coremark, fio, rsync, pgbench, sysbench ● Until then drop us an email at info@storpool.com Benchmarks
  • 28. 1. Your existing hardware can give you more a. See Venko's talk on KVM optimization (tomorrow 11am) b. fast networking (OVS-DPDK), fast storage (StorPool) 2. If you are building a new cloud - optimize for your use-case a. per-rack power limit b. per-core performance, per-core memory, per-core storage c. per-core cost Hardware
  • 29. Hardware optimization example: Xeon scalable selection
  • 30. ● Hardware ● Host OS and hypervisor (KVM) ● Virtual networking, service mesh ● Storage Optimization areas
  • 31. 1. Understanding performance 2. Benchmarks of public clouds 3. How to measure and optimize your own cloud 4. What's in a TCO 5. Conclusion Agenda
  • 32. ● Define minimum service level ● When comparing options use TCO tool (large spreadsheet) to find lowest cost per delivered unit of infrastructure (fixed-size VM/container with associated storage and networking) ● 100s of parameters ● Usable for both public and private scenarios TCO approach
  • 34. 1. Datacenter - power, cooling, max power per rack, remote hands 2. Compute - servers, CPUs, RAM, minimum core performance, cloud orchestration, management cost 3. Storage - storage servers, drives, software, management cost 4. Network - virtual network, CPU/RAM allocation, software, management cost - public/wide area network, IP transit cost What to include
  • 35. 1. Understanding performance 2. Benchmarks of public clouds 3. How to measure and optimize your own cloud 4. What's in a TCO 5. Conclusion Agenda
  • 36. 1. You can't judge a VM by its vCPUs and vRAM 2. Measure what matters to you 3. If you are a public or private cloud 2x,3x, higher application performance (per $ !) than hyperscalers is within reach. Half price for the same workload! 4. On your next project work with partners who understand performance. You can gain a lot! Conclusions
  • 39. Talk to us: The virtual booth at Data://Disrupted info@storpool.com https://storpool.com Thank you!