When optimizing Kubernetes applications, typically the goal is to find the configuration that assigns resources to containerized applications so as to minimize waste and ensure the quality of service.
Please refer to the Kubernetes optimization pack for the list of component types, parameters, metrics, and constraints.
Akamas offers different operators to configure Kubernetes entities. In particular, you can use the to update the definition file of a resource and apply it with the .
The following example is the definition of a deployment, where the replicas and resources are templatized in order to work with the FileConfigurator:
A typical workflow to optimize a Kubernetes application is usually structured as the following:
Configure the Kubernetes artifacts: use the to create the definition files starting from a template.
Apply the new parameters: apply the updated definitions using the .
Wait for the application to be ready: run a custom script to wait until the rollout is complete.
Here’s an example of a typical workflow for a system:
Akamas can access Kubernetes metrics using the This provider comes out of the box with a set of default queries to interrogate a Prometheus instance configured to fetch data from and .
Here’s a configuration example for a telemetry provider instance that uses Prometheus to extract all the Kubernetes metrics defined in this optimization pack:
where the configuration of the monitored component provides the additional filters as in the following snippet:
Please keep in mind that some resources, such as pods belonging to deployments, require wildcards in order to match the auto-generated names.
See this for an example of a study leveraging the Kubernetes pack.
apiVersion: apps/v1
kind: Deployment
metadata:
name: nginx-deployment
spec:
selector:
matchLabels:
app: nginx
replicas: ${deployment.k8s_workload_replicas}
template:
metadata:
labels:
app: nginx
spec:
containers:
- name: nginx
image: nginx:1.14.2
ports:
- containerPort: 80
resources:
requests:
cpu: ${container.cpu_request}
memory: ${container.memory_request}
limits:
cpu: ${container.cpu_limit}
memory: ${container.memory_limit}name: Kubernetes workflow
tasks:
- name: Configure deployment parameters
operator: FileConfigurator
arguments:
source:
path: nginx-deployment.yaml.templ
hostname: app.akamas.io
username: akamas
key: rsa-key
target:
path: nginx-deployment.yaml
hostname: app.akamas.io
username: akamas
password: akamas
- name: Apply parameters
operator: Executor
arguments:
command: kubectl apply -f nginx-deployment.yaml
host:
hostname: app.akamas.io
username: akamas
password: akamas
- name: Wait application ready
operator: Executor
arguments:
command: bash /home/akamas/app/check-status.sh
host:
hostname: app.akamas.io
username: akamas
password: akamas
- name: Run test
operator: Executor
arguments:
command: bash /home/akamas/app/run-test.sh
host:
hostname: app.akamas.io
username: akamas
password: akamasprovider: Prometheus
config:
address: monitoring.akamas.io
port: 9090name: nginx_pod
description: Pod running Nginx
componentType: Kubernetes Pod
properties:
prometheus:
job: 'kubernetes-cadvisor|kube-state-metrics'
namespace: akamas
pod: nginx-*name: cluster
description: Cluster
componentType: Kubernetes Cluster
properties:
prometheus:
job: 'kubernetes-cadvisor|kube-state-metrics'