Optimizing a live K8s deployment In this live optimization, the goal is to minimize the cost of a Kubernetes deployment with a single replica while matching, some performance-related constraints (e.g. the response time).
Notice that the workload selection refers to the service throughput metric provided by Istio.
Copy name : Optimize a Kubernetes Container
system : My Deployment
workflow : workflow
goal :
objective : minimize
function :
formula : container.cost
constraints :
absolute :
- name : response-time-slo
formula : shippingservice_istio.istio_incoming_response_time_90_ms:max <= 10
- name : shippingservice-restart-slo
formula : shippingservice_pod.k8s_pod_container_restarts:max == 0
- name : shippingservice-failures-slo
formula: shippingservice_istio.istio_incoming_failed_transactions / shippingservice_istio.istio_incoming_success_transactions <= 0.1
workloadsSelection :
- name : shippingservice_istio.istio_incoming_service_throughput
optimizerOptions :
onlineMode : RECOMMEND
experimentsWithBeta : 0
windowing :
type : trim
trim : [ 1m , 0s ]
task : Test
parametersSelection :
- name : shippingservice.cpu_request
domain : [ 10 , 600 ]
- name : shippingservice.memory_request
domain : [ 64 , 512 ]
parameterConstraints :
- name : limits_over_cpu_usage_bsl
formula : shippingservice.cpu_request >= 11
steps :
- name : baseline
type : baseline
numberOfTrials : 14
values :
shippingservice.cpu_request : 200
shippingservice.memory_request : 128
- name : optimize
type : optimize
numberOfTrials : 14
numberOfExperiments : 1000
numberOfInitExperiments : 0
maxFailedExperiments : 1000 An example of how this study can be extended to also include the JVM layer is provided by Optimizing a live full-stack deployment ( K8S + JVM ).