The document discusses optimizing application performance on Kubernetes, highlighting the importance of hardware tuning, observability, and proper resource requests and limits for pods. It presents various Kubernetes configurations, including deployment, limit ranges, and autoscaling mechanisms to enhance performance and minimize service disruption. Additionally, the document mentions using tools like Kruize for container optimization and stresses the significance of application-specific scaling metrics.

1. Optimizing Application
Performance on
Kubernetes
Dinakar Guniguntala @dinogun
Runtimes Cloud Architect, IBM

2. @dinogun 2
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3. @dinogun 3
Kubernetes is a portable,
extensible, open-source
platform for managing
containerized workloads
and services, that facilitates
both declarative configuration
… blah blah blah
Kitna Deti Hai ?*
Any questions ?
* What's the mileage ?

4. @dinogun 4
●
Throughput
●
Response Time
●
Utilization

5. @dinogun 5
Public
Private
Public
Public

6. @dinogun 6
BIOS
●
CPU Power and Performance Policy: <Performance>
OS / Hypervisor
●
CPU Scaling governor: <Performance>
$ cat /sys/devices/system/cpu/cpu0/cpufreq/scaling_available_governors
performance powersave
$ cat /sys/devices/system/cpu/cpu0/cpufreq/scaling_governor
Performance
Hyperthreading
●
Do not count hyperthreading while capacity planning
Don’t forget the hardware !

7. @dinogun 7
What is the granularity of observation ?
●
Trade-off between accurate info and overhead
Additional Operational Info
●
Spring Actuator
●
HealthCenter
Observability

8. @dinogun 8
QoS classes
Guaranteed
Burstable
BestEffort
Right Size !apiVersion: apps/v1
kind: Deployment
metadata:
name: acmeair
labels:
app: acmeair-app
spec:
replicas: 1
selector:
matchLabels:
app: acmeair-deployment
template:
metadata:
labels:
name: acmeair-deployment
app: acmeair-deployment
app.kubernetes.io/name: "acmeair-mono"
version: v1
spec:
volumes:
- name: test-volume
hostPath:
path: "/root/icp/jLogs"
type: ""
containers:
- name: acmeair-libertyapp
image: dinogun/acmeair-monolithic
imagePullPolicy: Always
ports:
- containerPort: 8080
resources:
requests:
memory: 500M
cpu: 2
limits:
memory: 1024M
cpu: 3
volumeMounts:
- name: "test-volume"
mountPath: "/opt/jLogs"
Ensure LimitRange does not get in
the way of your deployment !
apiVersion: v1
kind: LimitRange
metadata:
name: limit-range
spec:
limits:
- default:
cpu: 1
memory: 512Mi
defaultRequest:
cpu: 0.5
memory: 256Mi
type: Container
Requests → Should cover the observed peaks
Limits → Handle any spikes !

9. @dinogun 9
HPA, VPA and CAapiVersion: autoscaling/v2beta2
kind: HorizontalPodAutoscaler
metadata:
name: php-apache
spec:
scaleTargetRef:
apiVersion: apps/v1
kind: Deployment
name: php-apache
minReplicas: 1
maxReplicas: 10
metrics:
- type: Resource
resource:
name: cpu
target:
type: Utilization
averageUtilization: 50
- type: Pods
pods:
metric:
name: packets-per-second
target:
type: AverageValue
averageValue: 1k
- type: Object
object:
metric:
name: requests-per-second
describedObject:
apiVersion: networking.k8s.io/v1beta1
kind: Ingress
name: main-route
target:
type: Value
value: 10k
Set HPA with app specific metrics
- type: External
external:
metric:
name: concurrent_connections
selector: "connection=current"
target:
type: Value
Value: 1200
apiVersion: policy/v1beta1
kind: PodDisruptionBudget
metadata:
name: zk-pdb
spec:
maxUnavailable: 1
selector:
matchLabels:
app: zookeeper
Use PodDisruptionBudget with CA
to ensure no service disruption

10. @dinogun 10
Kruize
Right size and Optimize your Containers !
https://github.com/kruize/kruize

11. @dinogun 11
Summary
●
Don’t forget to tune the hardware
●
Observability is key
●
Ensure requests and limits are set for all app pods
●
Use app specific scaling metrics
●
Ensure no disruption with PDB
●
Kruize can help you optimize your runtime
– Eg. JVM tunables such as -XX:MaxRAMPercentage

12. @dinogun
12
Discount kitna
milega ?*
?!!!!
Questions → @dinogun
* Something tells me that doesn’t need to be translated !!