Achieving the Ultimate Performance with KVM
0 likes64 views
Many companies build new-age KVM clouds, only to find out that their applications & workloads do not perform well. In this talk we’ll show you how to get the most out of your KVM cloud and how to optimize it for performance: You’ll understand why performance matters and how to measure it properly. We’ll teach you how to optimize CPU and memory for ultimate performance and how to tune the storage layer for performance. You’ll find out what are the main components of an efficient new-age cloud and which network components work best. In addition, you’ll learn how to select the right hardware to achieve unmatched performance for your new-age cloud and applications. Venko Moyankov is an experienced system administrator and solutions architect at StorPool storage. He has experience with managing large virtualizations, working in telcos, designing and supporting the infrastructure of large enterprises. In the last year, his focus has been in helping companies globally to build the best storage solution according to their needs and projects.
![virtio vhost-net
● Two context switches
(optional):
1. virtio-net driver → KVM
2. KVM → virtio-net driver
(interrupt)
● shared memory with the host
kernel (vhost protocol)
● Allows Linux Bridge Zero
Copy
● qemu / virtio-net device is on
the control path only
● kernel thread [vhost] process
packets](https://image.slidesharecdn.com/storpoolpptachieving-the-ultimate-performance-with-kvm-venko-moyankov-210123105745/85/Achieving-the-Ultimate-Performance-with-KVM-22-320.jpg)
Ad
More Related Content
What's hot (20)
Similar to Achieving the Ultimate Performance with KVM (20)
![[OpenStack Day in Korea 2015] Track 1-6 - 갈라파고스의 이구아나, 인프라에 오픈소스를 올리다. 그래서 보이...](https://cdn.slidesharecdn.com/ss_thumbnails/16-150213044916-conversion-gate02-thumbnail.jpg?width=560&fit=bounds)
Ad
More from data://disrupted® (19)
Ad
Recently uploaded (20)
Achieving the Ultimate Performance with KVM
- 1. Achieving the Ultimate Performance with KVM Venko Moyankov data://disrupted 2020
- 2. About me ● Solutions Architect @ StorPool ● Network and System administrator ● 20+ years in telecoms building and operating infrastructures linkedin.com/in/venkomoyankov/ [email protected]
- 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
- 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 / multi-tenant environments) ● ROI, TCO - Lower cost per delivered resource (per VM) through higher density
- 5. Agenda ● Hardware ● Compute - CPU & Memory ● Networking ● Storage
- 6. Usual optimization goal - lowest cost per delivered resource - fixed performance target - calculate all costs - power, cooling, space, server, network, support/maintenance Example: cost per VM with 4x dedicated 3 GHz cores and 16 GB RAM Unusual - Best single-thread performance I can get at any cost - 5 GHz cores, yummy :) Compute node hardware
- 8. Compute node hardware Intel lowest cost per core: - Xeon Gold 5220R - 24 cores @ 2.6 GHz ($244/core) lowest cost per 3GHz+ core: - Xeon Gold 6240R - 24 cores @ 3.2 GHz ($276/core) - Xeon Gold 6248R - 24 cores @ 3.6 GHz ($308/core) lowest cost per GHz: - Xeon Gold 6230R - 26 cores @ 30 GHz ($81/GHz)
- 9. Compute node hardware AMD - EPYC 7702P - 64 cores @ 2.0/3.35 GHz - lowest cost per core - EPYC 7402P - 24 cores / 1S - low density - EPYC 7742 - 64 cores @ 2.2/3.4GHz x 2S - max density - EPYC 7262 - 8 cores @3.4GHz - max IO/cache per core, per $
- 10. Compute node hardware Form factor from to
- 11. Compute node hardware ● firmware versions and BIOS settings ● Understand power management -- esp. C-states, P-states, HWP and “bias” ○ Different on AMD EPYC: "power-deterministic", "performance-deterministic" ● Think of rack level optimization - how do we get the lowest total cost per delivered resource?
- 12. Agenda ● Hardware ● Compute - CPU & Memory ● Networking ● Storage
- 13. Tuning KVM RHEL7 Virtualization_Tuning_and_Optimization_Guide link https://pve.proxmox.com/wiki/Performance_Tweaks https://events.static.linuxfound.org/sites/events/files/slides/CloudOpen2013_Khoa_Huynh_v3.pdf http://www.linux-kvm.org/images/f/f9/2012-forum-virtio-blk-performance-improvement.pdf http://www.slideshare.net/janghoonsim/kvm-performance-optimization-for-ubuntu … but don’t trust everything you read. Perform your own benchmarking!
- 14. CPU and Memory Recent Linux kernel, KVM and QEMU … but beware of the bleeding edge E.g. qemu-kvm-ev from RHEV (repackaged by CentOS) tuned-adm virtual-host tuned-adm virtual-guest
- 15. CPU Typical ● (heavy) oversubscription, because VMs are mostly idling ● HT ● NUMA ● route IRQs of network and storage adapters to a core on the NUMA node they are on Unusual ● CPU Pinning
- 16. Understanding oversubscription and congestion Linux scheduler statistics: /proc/schedstat (linux-stable/Documentation/scheduler/sched-stats.txt) Next three are statistics describing scheduling latency: 7) sum of all time spent running by tasks on this processor (in ms) 8) sum of all time spent waiting to run by tasks on this processor (in ms) 9) # of tasks (not necessarily unique) given to the processor * In nanoseconds, not ms. 20% CPU load with large wait time (bursty congestion) is possible 100% CPU load with no wait time, also possible Measure CPU congestion!
- 17. Understanding oversubscription and congestion
- 18. Memory Typical ● Dedicated RAM ● huge pages, THP ● NUMA ● use local-node memory if you can Unusual ● Oversubscribed RAM ● balloon ● KSM (RAM dedup)
- 19. Agenda ● Hardware ● Compute - CPU & Memory ● Networking ● Storage
- 20. Networking Virtualized networking ● hardware emulation (rtl8139, e1000) ● paravirtualized drivers - virtio-net regular virtio vs vhost-net vs vhost-user Linux Bridge vs OVS in-kernel vs OVS-DPDK Pass-through networking SR-IOV (PCIe pass-through)
- 21. virtio-net QEMU ● Multiple context switches: 1. virtio-net driver → KVM 2. KVM → qemu/virtio-net device 3. qemu → TAP device 4. qemu → KVM (notification) 5. KVM → virtio-net driver (interrupt) ● Much more efficient than emulated hardware ● shared memory with qemu process ● qemu thread process packets
- 22. virtio vhost-net ● Two context switches (optional): 1. virtio-net driver → KVM 2. KVM → virtio-net driver (interrupt) ● shared memory with the host kernel (vhost protocol) ● Allows Linux Bridge Zero Copy ● qemu / virtio-net device is on the control path only ● kernel thread [vhost] process packets
- 23. virtio vhost-usr / OVS-DPDK ● No context switches ● shared memory between the guest and the Open vSwitch (requres huge pages) ● Zero copy ● qemu / virtio-net device is on the control path only ● KVM not in the path ● ovs-vswitchd process packets. ● Poll-mode-driver (PMD) takes 1 CPU core, 100%
- 24. PCI Passthrough ● No paravirtualized devices ● Direct access from the guest kernel to the PCI device ● Host, KVM and qemu are not on the data nor the control path. ● NIC driver in the guest ● No virtual networking ● No live migrations ● No filtering ● No control ● Shared devices via SR-IOV
- 25. Virtual Network Performance All measurements are between two VMs on the same host # ping -f -c 100000 vm2
- 27. virtio vhost-net qemu vhost thread
- 28. virtio vhost-usr / OVS-DPDK qemu OVS
- 29. Discussion ● Deep dive into Virtio-networking and vhost-net https://www.redhat.com/en/blog/deep-dive-virtio-networking-and-vhost-net ● Open vSwitch DPDK support https://docs.openvswitch.org/en/latest/topics/dpdk/
- 30. Agenda ● Hardware ● Compute - CPU & Memory ● Networking ● Storage
- 31. Storage - virtualization Virtualized live migration thin provisioning, snapshots, etc. vs. Full bypass only speed
- 32. Storage - virtualization Virtualized cache=none -- direct IO, bypass host buffer cache io=native -- use Linux Native AIO, not POSIX AIO (threads) virtio-blk vs virtio-scsi virtio-scsi multiqueue iothread vs. Full bypass SR-IOV for NVMe devices
- 33. Storage - vhost Virtualized with qemu bypass vhost before: guest kernel -> host kernel -> qemu -> host kernel -> storage system after: guest kernel -> storage system
- 34. storpool_server instance 1 CPU thread 2-4 GB RAM NIC storpool_server instance 1 CPU thread 2-4 GB RAM storpool_server instance 1 CPU thread 2-4 GB RAM • Highly scalable and efficient architecture • Scales up in each storage node & out with multiple nodes 25GbE . . . 25GbE storpool_block instance 1 CPU thread NVMe SSD NVMe SSD NVMe SSD NVMe SSD NVMe SSD NVMe SSD KVM Virtual Machine KVM Virtual Machine
- 35. 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 (example)
- 37. Latency ops per second best service lowest cost per delivered resource 37
- 38. Latency ops per second best service lowest cost per delivered resource only pain 38
- 39. Latency ops per second best service lowest cost per delivered resource only pain 39 benchmarks
- 40. Benchmarks
- 41. Real load
- 42. ?
- 43. ?