Kubernetes was originally targeted for running large scale web applications. I/O intensive workload represents a class of high-end applications such as network services, trading applications, database services that require high-speed access to hardware resources and often users specific hardware or CPU features to maximize their performance.

1. 1
All Clouds
Any Service
Unlimited Locations
Running I/O Intensive Workload On
Kubernetes
November 2019

2. 22
Introduction

3. 33
I/O Intensive Workload Defined
Networking
Low latency
ML Application
HPC Application

4. 44
Optimizing Kubernetes I/O Performance
Optimize the
container to host
performance ratio
The focus of this
presentation

5. 55
Normalising the workload management
I/O Intensive
Workload
Standard
Application
Highly Optimized
Infrastructure
Environment
Standard Public
Cloud
Highly Optimized
Public Cloud
Resources
Common automation through CI/CD pipeline
EaaS
The focus of this
presentation
QA vs Production
Environment
Best Cost/Performance

6. 6
Optimizing Kubernetes
Performance through H/W
acceleration

7. 77
Kubernetes H/W Acceleration Features
HW Platform Intel® Ethernet
Network Adapter
Intel® QuickAssist
Technology
Intel® DC SSD Intel® Programmable
Acceleration Card
▪ Multiple network interfaces for VNFs
▪ High performance Data Plane (E-W, N-S)
▪ Node Feature Discovery - platform capabilities
▪ Device Plugin (SR-IOV, Intel® QAT, FPGA…)
▪ CPU Core-Pinning and isolation for K8s pods
▪ Huge Page allocation
▪ Topology Manager - Guarantee NUMA alignment
▪ Platform Telemetry
▪ Installation playbook

8. 88
Offloading SSL
Encryption
Offloading
Network
Packets

9. 99
Benchmark Use CaseAppsSWPlatform
Orchestration
HW Platform
Policy Driven for Multi-Cloud Services
HTTPS
Bench
Intel® Ethernet
Network Adapter
Intel® QuickAssist
Technology
Intel® DC SSD Intel® Programmable
Acceleration Card
GitHub
▪ Multiple network interfaces for VNFs
▪ High performance Data Plane (E-W, N-S)
▪ Node Feature Discovery - platform capabilities
▪ Device Plugin (SR-IOV, Intel® QAT, FPGA…)
▪ CPU Core-Pinning and isolation for K8s pods
▪ Huge Page allocation
▪ Topology Manager - Guarantee NUMA alignment
▪ Platform Telemetry
▪ Installation playbook
TOSCA
Blueprint
Crypto
Crypto
Open sourced,
up-streamed
Reference
Implementation
for Kubernetes*
Scaled through
Intel® Network
Builders and Intel®
Select Solutions

10. 10
I/O Optimised K8S Cluster Hardware Architecture
QAT Enabled NodeStandard Node

11. 11
Phase 1 – Setting up the I/O Optimised K8S
Environment
11
Running I/O Intensive cloud
native workload on private
cloud while being connected to
data services on public cloud
The Environment Includes:
○ Multi Cloud: AWS / Azure , OpenStack
○ Multi Cloud Networking.
○ Kubernetes Cluster Optimised for I/O
Intensive workload
○ DBaaS - on VM’s
EaaS - Environment As a Service
I/O Intensive Cloud Native Stack
AWS
VPC, Load Balancer
Azure
US East, West
Dev, Test, Prod
OpenStack
Kubernetes DBaaSAnsible
VPN
Private

12. 1212
https://youtu.be/PxRxbhA6gCI
Creating the Environment High Level Overview

13. 1313
Leveraging Kubernetes Node Feature Discovery
○ Each node is labeled with a list of hardware capabilities
○ APP A —> SRIOV & Turbo Boost is supported only by node 2.
Node Feature Discovery in Kubernetes
Cluster Master
Node 1
Node 2
SR-IOV
Application A
Turbo Boost
node selector
Node 2
NFD
DISCOVERY
POD
SR-IOV
Turbo Boost
NUMA
QAT
IOMMU
Application B
Node 1
NFD
DISCOVERY
POD
QAT
Turbo Boost
IOMMU
QAT
Application B
IOMMU
node selector
Application AKubernetes Node labels

14. 14
Expanding to Public Cloud
Workloads placement utilizing Enhanced Platform Awareness(Intel EPA)

15. | ©2019 F5 NETWORKS15
Public Cloud
KUBERNETES CLUSTER
QAT
Node
Node
BIG-IP
Virtual Edition(s)
F5 CONTAINER
INGRESS SERVICES
Inspecting
Traffic
K8s Node Labels
COTS/COTS+ matchmaking
Ingress examples
Security (PAC, DDOS)
Encrypt/Decrypt (QAT)
Traffic steering (at F5 booth)
Traffic re-encrypted
(using Intel®
QAT
offload) and sent to
another destination in
public cloud
Encrypted
Unencrypted
QuickAssist
Technology
<none>
EPA, DPDK ,
AWS ENA
Demo setup
Cloudify Leverages Node
Feature Discovery
to Optimize NGINX Service
Placement
Node feature
discovery (NFD)
Apache
Benchmark
Apache
Benchmark
Server

16. 16
Node labels & POD placement using “nodeSelector”
16
Node Capabilities Application (POD) Requirements
QAT Enabled
Node
Capabilities
QAT
Enabled
POD

17. 1717
Intent based placement

18. 18
Phase 2 – Placement Policy
18
NGINX deployed
on QAT K8S node
NGINX deployed
on any K8S node

19. 1919
https://youtu.be/3WlLg6u6ef4
QAT Enabled Environment Detailed Overview

20. 20
Hardware Architecture Performance Results
2X Performance Gain

21. 2121
Normalising the workload management
I/O Intensive
workload
Standard
Application
Highly Optimized
Infrastructure
Environment
Standard Public
Cloud
Highly Optimized
Public Cloud
Resources
Common Automation through CI/CD pipeline
EaaS
The focus of this
presentation
QA vs Production
Environment
Best Cost/Performance

22. 2222
EaaS - Environment As a Service
22
EaaS - Environment As a Service
Web App
Environment
HPC
Environment
Analytics
Environment
Dev/ QA/
Prod
F5, VPC, Express
Route
CI / CD
US East, West
Dev, Test, Prod
Palo Alto, V-WAN
US East, West,
Asia
Prod
Dedicated
Interconnect
US East, West,
Europe, Asia
Prod
One Pipeline, Multiple Environments
Decoupling the CI/CD pipeline from the
infrastructure environment.
Package each environment into high level
reusable services
Each environment serves many applications
We call it Environment As a Service - EaaS
Intelligent automation - reduced deployments by
98%, from 5 days to 2.5 hours.
Achieved 60% More Velocity & Accelerated Time
to Value with Cloudify. (FiCO)
The Results:

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Why Cloudify ?
23
Cloudify Is leading at
Orchesting
Heterogeneous and
Distributed Services
Multi Cloud
Multi Site Clusters
Deployments
Edge
DistributedCentralised
Heterogeneous
(Multi site/Cloud)
Homogeneous

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Key Customers and Strategic Alliances
24

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Thank You.