ottoscalr

Installation Guide

Ottoscalr can be installed in any kubernetes cluster which meets the prerequisites. Since its functionality relies upon the availability of historical data for the recommendations to be generated, a promql compliant time series database is needed.

Prerequisites

Prometheus metrics required

For ottoscalr to scrap cpu utilization data, cpu redline breach data and pod ready time data, certain metrics should be present in your Promql complaint metrics source. Go through all the metrics required list. These metrics are required for ottoscalr to function.

Based on the above metrics, these are the promql queries that scrap cpu utilization data, cpu redline breach data and pod ready time data for a particular deployment. Do execute them in your Promql complaint metrics source to make sure that these queries provide the expected values.

Building the image

The following section outlines the docker image build process for the ottoscalr. The following commands builds the image for the platform linux/amd64. Change the TARGETOS and TARGETARCH if it is required to be built for other platform. Run this from the root directory of the project. Give your repository and tag for the image which will be built and pushed.

$ TARGETOS=linux;TARGETARCH=amd64;make docker-build docker-push IMG={repository}:{tag}

Deploying the ottoscalr

Ottoscalr can be deployed via this helm chart.

This chart bootstraps ottoscalr on a Kubernetes cluster using the Helm package manager. As part of that, it will install all the required Custom Resource Definitions (CRD) which introduces the following kinds:

apiVersion: ottoscaler.io/v1alpha1
kind: PolicyRecommendation

apiVersion: ottoscaler.io/v1alpha1
kind: Policy

Installing the Chart

To install the chart with the release name ottoscalr:

$ kubectl create namespace ottoscalr
$ helm install ottoscalr <path-of-the-helm-chart> --namespace ottoscalr -f <path-of-the-helm-chart>/values.yaml

Upgrading the Chart

To update the already installed chart ottoscalr:

$ helm upgrade ottoscalr <path-of-the-helm-chart> --namespace ottoscalr -f <path-of-the-helm-chart>/values.yaml

Uninstalling the Chart

To uninstall/delete the ottoscalr Helm chart:

helm uninstall ottoscalr

The command removes all the Kubernetes components associated with the chart and deletes the release.

Configuration

The following table lists the configurable parameters of the KEDA chart and their default values.

Ottoscalr Deployment

Parameter Type Default Description
image.repository string "" Image name of ottoscalr deployment
image.tag string "" Image tag of ottoscalr deployment
replicaCount int 1 Capability to configure the number of replicas for ottoscalr operator. While you can run more replicas of our operator, only one operator instance will be the leader and serving traffic. You can run multiple replicas, but they will not improve the performance of ottoscalr, it could only reduce downtime during a failover.
resources object {"limits":{"cpu":2,"memory":"4Gi"},"requests":{"cpu":"2","memory":"4Gi"}} Manage resource request & limits of ottoscalr operator pod
serviceMonitor.create bool true If true, this will export ottoscalr metrics.

Operations

Parameter Type Default Description
ottoscalr.config.metricsScraper.prometheusUrl string "" URL where prometheus for the kubernetes cluster is running. Fetching metrics from a single or multiple prometheus instance(give comma separated urls) is supported. Metrics from multple prometheus instances will be aggregated. If you have 2 instances named p8s1 and p8s2, it should be added like "p8s1,p8s2"
ottoscalr.config.metricsScraper.queryTimeoutSec int 300 Time in seconds within which the response for any query should be served by the prometheus
ottoscalr.config.metricsScraper.querySplitIntervalHr int 8 The shortest period in hour for which data will be fetched from prometheus. If we are fetching data for 28 days, it will be divided into (28*24)/8 intervals and parallely data for all the intervals will be fetched and merged finally. This is required to execute the recommendation workflow faster.
ottoscalr.config.policyRecommendationController.maxConcurrentReconciles int 1 Maximum number of concurrent Reconciles of policy recommendation controller which can be run.
ottoscalr.config.policyRecommendationController.minRequiredReplicas int 3 The hpa.spec.minReplicas recommended by the controller will not have replicas minimum than this.
ottoscalr.config.policyRecommendationController.policyExpiryAge string 3h Target Recommendation will be reached in multiple iterations and through different policies. This is the time after which a policy expires and next policy in the list can be applied.
ottoscalr.config.breachMonitor.pollingIntervalSec int 300 Time in seconds to check for any breaches happened during this time
ottoscalr.config.breachMonitor.cpuRedLine float 0.75 Conisder it a breach if CPU utilization goes above this number.
ottoscalr.config.breachMonitor.concurrentExecutions int 50 Concurrent execution of breach queries. Configure it according to the number of concurrent connections that can be handled safely by the prometheus
ottoscalr.config.periodicTrigger.pollingIntervalMin int 180 Duration in minutes to periodically trigger the recommendation generation
ottoscalr.config.policyRecommendationRegistrar.excludedNamespaces string "" Comma separated namespaces where ottoscalr will not generate recommendations. Example: “namespace1,namespace2,namespace3”
ottoscalr.config.cpuUtilizationBasedRecommender.metricWindowInDays int 28 Number of days for which cpu utilization metrics should be fetched for generating the recommendation.
ottoscalr.config.cpuUtilizationBasedRecommender.minTarget int 5 The cpu utilization threshold recommended will not be less than this.
ottoscalr.config.cpuUtilizationBasedRecommender.maxTarget int 60 The cpu utilization threshold recommended will not be more than this.
ottoscalr.config.cpuUtilizationBasedRecommender.metricsPercentageThreshold int 25 Minimum percentage of metrics to be present to generate any recommendation. Example: if ottoscalr.config.cpuUtilizationBasedRecommender.metricWindowInDays is 28 days and ottoscalr.config.cpuUtilizationBasedRecommender.metricsPercentageThreshold is 25%, then atleast 7 days worth of data should be present.
ottoscalr.config.hpaEnforcer.maxConcurrentReconciles int 1 Maximum number of concurrent Reconciles which can be run by hpaenforcement controller
ottoscalr.config.hpaEnforcer.excludedNamespaces string "" Comma separated namespaces where ottoscalr will not create HPAs. Example: “namespace1,namespace2,namespace3”
ottoscalr.config.hpaEnforcer.includedNamespaces string "" If provided, ottoscalr will only create HPAs for these namespaces. If it is empty, it will include all except for the excluded ones. Example: “namespace1,namespace2,namespace3”
ottoscalr.config.hpaEnforcer.minRequiredReplicas int 2 the hpa will be created only if the min replicas generated by the policy recommendation controller is greater than this.
ottoscalr.config.hpaEnforcer.whitelistMode bool true hpa controller will act only on deployments having ottoscalr.io/enable-hpa-enforcement: true annotation and create hpa for them. If false, hpaEnforcer runs on blacklistMode where it will create hpas for every workload except for ones having ottoscalr.io/disable-hpa-enforcement: true. It is recommended to run with whitelistMode as true.
ottoscalr.config.hpaEnforcer.isDryRun bool false If true, hpa controller will not create any HPAs.
ottoscalr.config.enableMetricsTransformer bool true This metrics transformer can be used to interpolate any known period of data that should not be used for generating recommendation.
ottoscalr.conifg.eventCallIntegration.customEventDataConfigMapName string custom-event-data-config This configmap will be deployed as part of the helm chart, please do not change. You can add any period of data to be interpolated in the configmap in the following format 7f8b9c83: '{"eventId":"7f8b9c83","eventName":"Outlier","startTime":"2023-07-27 04:00","endTime":"2023-07-27 05:00"}'. Keep the startTime and endTime in this YYYY-MM-DD HH:MM. Similarly, multiple such events can be added. To add, edit the data in the configmap customeventdataconfig.yaml in the helm templates.
ottoscalr.config.autoscalerClient.scaledObjectConfigs.enableScaledObject bool false Flag whether to use KEDA ScaledObjects or HPA for autoscaling. If false, HPA client will be used. KEDA needs to be deployed on your cluster for enabling it.
ottoscalr.config.autoscalerClient.hpaConfigs.hpaAPIVersion string "v2" Set this if using HPA for autoscaling. By default, autoscaling/v2 api is supported. If you wish to use autoscaling/v1 api for HPA, change this to "v1".
ottoscalr.config.enableArgoRolloutsSupport bool false Change this to true if you have support for Argo Rollouts.
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