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Metrics towards enterprise readiness of unikernels

Evaluation of 3 platforms (VM, container, unikernel) using subset of metrics important to 3 sets of enterprise stakeholders: developers/DevOps, CIO, and customers.

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Metrics towards Enterprise
Readiness of Unikernels
Rean Griffith, Madhuri Yechuri
1
Agenda
Introduction - bios
Unikernel Background
Developer/DevOps care about
Metric Set 1: Application lifecycle overhead
CIO cares about
Metric Set 2: Application datacenter footprint
2
Unikernel Background
Working Definition: Single Process env for running code
3
Unmodified Legacy App
support
Multi-threaded App support
OSv Partial Yes (1: glibc subset, no
fork/exec)
Yes* (pthread subset)
MirageOS No* (until non-OCAML language
bindings are available, no
fork/execve)
Green threads (event loop) only
Rumprun Yes* (no
fork/execve/sigaction/mmap)
Yes (pthread)
Developer/DevOps care about
Enterprise Application Lifecycle management
Developer: Time to build app from source code, preferably unmodified
DevOps: Time to configure runtime parameters (ex: TCP port, log file
location)
DevOps: Time to deploy application
DevOps: Qualitative ease of managing+debugging long-running (weeks /
months) application 4
App Lifecycle Experiment Environment
Machine
CPU: Intel(R) Core(TM) i3 CPU M 380 @ 2.53GHz
Memory: 4GB RAM
OS: Ubuntu 16.04 LTS
Deployment Options (local image)
VM: VirtualBox 5.0 (Hashicorp Alpine 3.4), Vagrant 1.8.1, Nginx 1.10.1-r1, pre-pulled image, 1GB VM
Container: Docker 1.12.0-rc2, pre-pulled Official Nginx:latest (1.11.1) image
Unikernel: OSv 0.24, KVM + QEMU 2.5.0, pre-pulled Nginx 1.9.15 image, 2GB VM
5
Metric Set 1: Application Lifecycle
Convert
Code to
Image
(Hours)
VM 8 (1 , 2,
3)
Container 0
Unikernel 40 ( 1, 2)
6
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Metrics towards enterprise readiness of unikernels

  • 1. Metrics towards Enterprise Readiness of Unikernels Rean Griffith, Madhuri Yechuri 1
  • 2. Agenda Introduction - bios Unikernel Background Developer/DevOps care about Metric Set 1: Application lifecycle overhead CIO cares about Metric Set 2: Application datacenter footprint 2
  • 3. Unikernel Background Working Definition: Single Process env for running code 3 Unmodified Legacy App support Multi-threaded App support OSv Partial Yes (1: glibc subset, no fork/exec) Yes* (pthread subset) MirageOS No* (until non-OCAML language bindings are available, no fork/execve) Green threads (event loop) only Rumprun Yes* (no fork/execve/sigaction/mmap) Yes (pthread)
  • 4. Developer/DevOps care about Enterprise Application Lifecycle management Developer: Time to build app from source code, preferably unmodified DevOps: Time to configure runtime parameters (ex: TCP port, log file location) DevOps: Time to deploy application DevOps: Qualitative ease of managing+debugging long-running (weeks / months) application 4
  • 5. App Lifecycle Experiment Environment Machine CPU: Intel(R) Core(TM) i3 CPU M 380 @ 2.53GHz Memory: 4GB RAM OS: Ubuntu 16.04 LTS Deployment Options (local image) VM: VirtualBox 5.0 (Hashicorp Alpine 3.4), Vagrant 1.8.1, Nginx 1.10.1-r1, pre-pulled image, 1GB VM Container: Docker 1.12.0-rc2, pre-pulled Official Nginx:latest (1.11.1) image Unikernel: OSv 0.24, KVM + QEMU 2.5.0, pre-pulled Nginx 1.9.15 image, 2GB VM 5
  • 6. Metric Set 1: Application Lifecycle Convert Code to Image (Hours) VM 8 (1 , 2, 3) Container 0 Unikernel 40 ( 1, 2) 6
  • 7. Metric Set 1: Application Lifecycle Convert Code to Image (Hours) Start Time (Seconds) VM 8 (1 , 2, 3) 66.557 Container 0 1.113 Unikernel 40 ( 1, 2) 0.483 7
  • 8. Metric Set 1: Application Lifecycle Convert Code to Image (Hours) Start Time (Seconds) Stop Time (Seconds) VM 8 (1 , 2, 3) 66.557 7.478 Container 0 1.113 0.685 Unikernel 40 ( 1, 2) 0.483 0.019 8
  • 9. Metric Set 1: Application Lifecycle Convert Code to Image (Hours) Start Time (Seconds) Stop Time (Seconds) Debuggability VM 8 (1 , 2, 3) 66.557 7.478 Container 0 1.113 0.685 Unikernel 40 ( 1, 2) 0.483 0.019
  • 10. CIO cares about Consolidation of applications on finite hardware resources Multi-tenant security isolation amongst applications on a compute node Multi-tenant Resource Management Manageability, Accounting, Auditability Infrastructure Power consumption 10
  • 11. Metric Set 2: Data center footprint Image Size (MB) VM 143 Container 182.8 Unikernel 7.8
  • 12. Metric Set 2: Data center footprint Image Size (MB) Runtime Memory Overhead (MB) VM 143 619 (/proc/{vboxpid}/status/{V mSize} - Configured) Container 182.8 274.4 (containerd-shim /proc/{pid}/status/{VmSize }) Unikernel 7.8 1222 (/proc/{qemupid}/status/{V mSize} - Configured)
  • 13. Metric Set 2: Data center footprint Image Size (MB) Runtime Memory Overhead (MB) Security (Tenant Isolation) VM 143 619 (/proc/{vboxpid}/status/{V mSize} - Configured) Strong Container 182.8 274.4 (containerd-shim /proc/{pid}/status/{VmSize }) Weak Unikernel 7.8 1222 (/proc/{qemupid}/status/{V mSize} - Configured) Strong
  • 14. Metric Set 2: Data center footprint Image Size (MB) Runtime Memory Overhead (MB) Security (Tenant Isolation) Resource Knobs VM 143 619 (/proc/{vboxpid}/status/{V mSize} - Configured) Strong Strong (Reservation, Limits) Container 182.8 274.4 (containerd-shim /proc/{pid}/status/{VmSize }) Weak Moderate (Limits) Unikernel 7.8 1222 (/proc/{qemupid}/status/{V mSize} - Configured) Strong Moderate (knobs available, not used yet)
  • 15. Customer cares about Application Performance Resource Isolation Security Application high-availability 15
  • 16. Performance Experiment Environment Machine Lenovo W520, CPU: Intel i7-2760QM CPU 2.40 GHz x 8 logical cores, Memory: 19.5 GB RAM OS: Ubuntu 16.04.1 LTS (64-bit) Deployment Options Linux (host machine) - Ubuntu 16.04.1 LTS, Linux kernel: 4.4.0-34-generic #53-Ubuntu SMP VM: VirtualBox (v5.1.2) - Ubuntu 16.04.1 LTS, Linux kernel: 4.4.0-34-generic #53-Ubuntu SMP, 8GB RAM, 4 vCPUs Container: Docker (v1.12.0) - Linux kernel: 4.4.0-34-generic #53-Ubuntu SMP Unikernel: OSv (based on git hash: f53c0c39) - v0.24-176-g2e19ba4 (Ubuntu 16.04.1 LTS, Linux kernel: 4.4.0-34-generic #53-Ubuntu SMP), 4 vCPUs, 2GB RAM 16
  • 17. Metric Set 3: Application Performance 17
  • 18. Metrics Set 3: Throughput Explanation nginx-osv > nginx-linux > nginx-docker > nginx-vm Baseline: 1 thread/client Nginx-linux (bare metal) ~600 requests/sec Nginx-vm slightly lower: expected because the client request needs to traverse two I/O stacks - the hypervisor’s and the Guest OS’s Nginx-docker is close to bare metal: expected since the only thing separating the container from the workload generator is a network bridge Nginx-osv slightly better than bare metal: client requests still have to go through the unikernel’s I/O stack but the I/O stack for OSV was designed to be light/lower-overhead - influenced by a design based on Van Jacobson’s net channels 10 threads Results get slightly more than 10X better (this is mostly because of reductions in average latency - next graph) but the ordering remains the same 18
  • 19. Metrics Set 3: Response Time Explanation nginx-osv > nginx-linux > nginx-docker > nginx-vm Overall response times between 1ms and 2ms Single thread case ~1.5ms, and 10 thread case < 1.5ms Reduction in response time moving 1 to 10 threads is mostly a result of caching and multiplexing. With multiple threads, more work gets done per-unit time. While thread A is processing the results of a response, thread B, which was waiting, can quickly be given a cached copy of the static file being served. 19
  • 20. Summary Developer/DevOps care about Metric Set 1: Application lifecycle overhead CIO cares about Metric Set 2: Application datacenter footprint Customer cares about Metric Set 3: Application performance 20
  • 21. Acknowledgements Thank you! OSv: Nadav Har’El Defer Panic: Ian Eyberg SF Unikernels Meetup Mike Larkin, Carl Waldspurger, Anne Holler 21
  • 22. Links Rain Workload Toolkit Nginx VirtualBox repo Nginx OSv OSv networking hack Performance evaluation of OSv OSv USENIX paper 22
  • 23. Q & A Madhuri cosmokramer@gmail.com GitHub: myechuri Rean rean@caa.columbia.edu GitHub: rean 23

Editor's Notes

  1. Owner: Rean Note: Refer to image size and overhead for cost estimates.
  2. Worker connections = #clients simultaneously served Worker processes * worker connections = anticipated upper limit on reqs/sec Workload version of Rain (git hash b0b29438) Workload configuration files: https://github.com/rean/rain-workload-toolkit/blob/master/config/rain.config.nginx.json (determines workload duration, warm up and warm down) https://github.com/rean/rain-workload-toolkit/blob/master/config/profiles.config.nginx.json (controls the IP address and port, number of threads, workload generator to use)
  3. Experiment description * simple HTTP GET workload, run for 5 minutes (10 sec warmup before, 10 sec rampdown afterwards) x 5 repeats * Load generator and nginx instance run on the same machine so there’s no network jitter. We’re mainly capturing I/O stack overheads/differences * Results reported = average over 5 repeats, error bars are 95% confidence intervals Response time results * 1 thread/client is the baseline case * bare metal (Nginx-linux) ~600 requests/sec, Nginx-vm slightly lower (expected because the client request needs to traverse two I/O stacks - the hypervisor’s and the Guest OS’s), Nginx-docker is close to bare metal (expected since the only thing separating the container from the workload generator is a network bridge), Nginx-osv slightly better than bare metal (client requests still have to go through the unikernel’s I/O stack but the I/O stack for OSV was designed to be light/lower-overhead - influenced by a design based on Van Jacobson’s net channels) * General ordering is nginx-osv > nginx-linux > nginx-docker > nginx-vm * 10 threads * Results get slightly more than 10X better (this is mostly because of reductions in average latency - next graph) but the ordering remains the same nginx-osv > nginx-linux > nginx-docker > nginx-vm Response time results * Overall response times between 1ms and 2ms * Single thread case ~1.5ms, and 10 thread case < 1.5ms * The reduction in response time moving 1 to 10 threads is mostly a result of caching and multiplexing. With multiple threads more work gets done per-unit time. While thread A is processing the results of a response, thread B, which was waiting, can quickly be given a cached copy of the static file being served.
  4. Summarize: 3 perspectives on what might be important (CIO, developer, customer). Measurements.