Merge pull request #3914 from wangchen615/customizable_recommender_kep
Add enhancement proposal for feature request #3913
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# Support Customized Recommenders for Vertical Pod Autoscalers
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<!-- toc -->
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- [Summary](#summary)
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- [Motivation](#motivation)
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- [Goals](#goals)
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- [Non-Goals](#non-goals)
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- [Proposal](#proposal)
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- [User Stories](#user-stories-optional)
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- [Story 1](#story-1)
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- [Story 2](#story-2)
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- [Implementation Details](#implementation-details)
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- [Deployment Details](#deployment-details)
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- [Design Details](#design-details)
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- [Test Plan](#test-plan)
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- [Upgrade / Downgrade Strategy](#upgrade--downgrade-strategy)
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- [Alternatives](#alternatives)
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- [Out of Scope](#out-out-scope)
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- [Implementation History](#implementation-history)
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<!-- /toc -->
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## Summary
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Today, the current VPA recommends CPU/Mem requests based on one recommender,
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which recommends the future requests based on the historical usage observed in a
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rolling time window. As there is no universal recommendation policy that applies to all
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types of workload, this KEP suggests supporting multiple customized recommenders in VPA.
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Thus, users can run different recommenders for different workloads, as they may exhibit
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very distinct resource usage behaviors.
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## Motivation
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A VPA is used to recommend the requested resources of containers in pods when the actual CPU/memory usage of a container
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is significantly different from the resources requested. Resource usage-based recommendation
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is the basic approach that resizes containers according to the actual usage observed and is
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implemented in the default VPA recommender. Users can configure the time window and a certain
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percentile of observed usage in the past as the prediction of future requests/limits for CPU/memory.
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However, as containers running different types of workloads may have different resource usage patterns,
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there is no universal policy that applies to all. The existing VPA recommender may not accurately
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predict future resource usage when containers exhibit certain resource usage behaviors,
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such as trending, periodically changing, or occasional spikes, resulting in significant
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over-provisioning and OOM kills for microservices. Learning different types of resource usage
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behaviors for workloads and applying different algorithms to improve resource utilization
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(CPU and Memory) predictions can significantly reduce over-provisioning and OOM kills in VPA.
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### Goals
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- Allow the VPA object to specify a customized recommender to use.
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- Allow the VPA object to use the default recommender when no recommender is specified.
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### Non-Goals
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- We assume no pod uses two recommenders at the same time.
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- We do not resolve conflicts between recommenders.
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## Proposal
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### User Stories
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#### Story 1
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- Containers with Cyclic Patterns in Resource Usage
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Containers used in monitoring may receive load periodically to process but need to be long-running
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to listen to incoming traffic. Thus, these containers usually exhibit cyclic patterns, alternating
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between usage spikes and idling. Resizing containers according to usage observed in the previous
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time window may always lead to under-provision for a short period when the load spikes just arrive.
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The problem will happen for memory if the cyclic pattern length is >8 days. For CPU, the problem may
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be visible for example with lower usage on the weekend. The problem will even lead to frequent pod evictions
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when the pod was resized according to the idling period and the host resource has been taken by other pods.
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#### Story 2
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- Containers with Different but Recurrent Behaviors in Resource Usage
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Containers running spark/deep learning training workloads are known to show recurring and repeating
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patterns in resource usage. Prior research has shown that different but recurrent behaviors occur
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for different containerized tasks, such as Spark or deep learning training. These common patterns can
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be represented by phases, which display similar resource usage of computational resources over time.
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There are common sequences of patterns for different executions of the workload and they can be used
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to proactively predict future resource usage more accurately. The default recommender in the current
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VPA adopts a reactive approach so a more proactive recommender is needed for these types of workload.
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### Implementation Details
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The following describes the details of implementing a first-citizen approach to support the customized
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recommender. Namely, a dedicated field `recommenderName` is added to the VPA crd definition in
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`deploy/vpa-v1.crd.yaml`.
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```yaml
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validation:
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# openAPIV3Schema is the schema for validating custom objects.
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openAPIV3Schema:
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type: object
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properties:
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spec:
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type: object
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required: []
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properties:
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recommenderName:
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type: string
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targetRef:
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type: object
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updatePolicy:
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type: object
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```
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Correspondingly, the `VerticalPodAutoscalerSpec` in `pkg/apis/autoscaling.k8s.io/v1/types.go`
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should be updated to include the `recommenderName` field.
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```golang
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// VerticalPodAutoscalerSpec is the specification of the behavior of the autoscaler.
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type VerticalPodAutoscalerSpec struct {
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// TargetRef points to the controller managing the set of pods for the
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// autoscaler to control - e.g. Deployment, StatefulSet. VerticalPodAutoscaler
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// can be targeted at controller implementing scale subresource (the pod set is
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// retrieved from the controller's ScaleStatus) or some well known controllers
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// (e.g. for DaemonSet the pod set is read from the controller's spec).
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// If VerticalPodAutoscaler cannot use specified target it will report
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// ConfigUnsupported condition.
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// Note that VerticalPodAutoscaler does not require full implementation
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// of scale subresource - it will not use it to modify the replica count.
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// The only thing retrieved is a label selector matching pods grouped by
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// the target resource.
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TargetRef *autoscaling.CrossVersionObjectReference `json:"targetRef" protobuf:"bytes,1,name=targetRef"`
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// Describes the rules on how changes are applied to the pods.
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// If not specified, all fields in the `PodUpdatePolicy` are set to their
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// default values.
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// +optional
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UpdatePolicy *PodUpdatePolicy `json:"updatePolicy,omitempty" protobuf:"bytes,2,opt,name=updatePolicy"`
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// Controls how the autoscaler computes recommended resources.
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// The resource policy may be used to set constraints on the recommendations
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// for individual containers. If not specified, the autoscaler computes recommended
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// resources for all containers in the pod, without additional constraints.
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// +optional
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ResourcePolicy *PodResourcePolicy `json:"resourcePolicy,omitempty" protobuf:"bytes,3,opt,name=resourcePolicy"`
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// Name of the recommender responsible for generating recommendation for this object.
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RecommenderName []string `json:"recommenderName,omitempty" protobuf:"bytes,4,opt,name=recommenderName"`
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}
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```
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When creating a recommender object for recommendations, the recommender main routine should initiate itself
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with a predefined recommender name, which can be defined as a constant in the `pkg/recomender/main.go` routine,
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```golang
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const RecommenderName = "default"
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recommender := routines.NewRecommender(config, *checkpointsGCInterval, useCheckpoints, RecommenderName, *vpaObjectNamespace)
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```
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where the routines.NewRecommender can pass the `RecommenderName` to the clusterState object.
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```golang
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// NewRecommender creates a new recommender instance.
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// Dependencies are created automatically.
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// Deprecated; use RecommenderFactory instead.
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func NewRecommender(config *rest.Config, checkpointsGCInterval time.Duration, useCheckpoints bool, recommender_name string, namespace string) Recommender {
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clusterState := model.NewClusterState(recommender_name)
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return RecommenderFactory{
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ClusterState: clusterState,
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ClusterStateFeeder: input.NewClusterStateFeeder(config, clusterState, *memorySaver, namespace),
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CheckpointWriter: checkpoint.NewCheckpointWriter(clusterState, vpa_clientset.NewForConfigOrDie(config).AutoscalingV1()),
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VpaClient: vpa_clientset.NewForConfigOrDie(config).AutoscalingV1(),
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PodResourceRecommender: logic.CreatePodResourceRecommender(),
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CheckpointsGCInterval: checkpointsGCInterval,
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UseCheckpoints: useCheckpoints,
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}.Make()
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}
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// NewClusterState returns a new ClusterState with no pods.
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func NewClusterState(recommender_name string) *ClusterState {
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return &ClusterState{
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RecommenderName: recommender_name,
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Pods: make(map[PodID]*PodState),
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Vpas: make(map[VpaID]*Vpa),
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EmptyVPAs: make(map[VpaID]time.Time),
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aggregateStateMap: make(aggregateContainerStatesMap),
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labelSetMap: make(labelSetMap),
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}
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}
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```
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Therefore, when loading VPA objects to the `clusterStateFeeder`, it can use the field selector to select VPA CRDs that
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have `recommenderName` equal to the current clusterState’s `RecommenderName`.
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```golang
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// Fetch VPA objects and load them into the cluster state.
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func (feeder *clusterStateFeeder) LoadVPAs() {
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// List VPA API objects.
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allVpaCRDs, err := feeder.vpaLister.List(labels.Everything())
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if err != nil {
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klog.Errorf("Cannot list VPAs. Reason: %+v", err)
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return
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}
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var vpaCRDs []*vpa_types.VerticalPodAutoscaler
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for _, vpaCRD := range allVpaCRDs {
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currentRecommenderName := feeder.clusterState.RecommenderName
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if (vpaCRD.Spec.RecommenderName != currentRecommenderName) && (vpaCRD.Spec.RecommenderName != "") {
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klog.V(6).Infof("Ignoring the vpaCRD as its name %v is not equal to the current recommender's name %v", vpaCRD.Spec.RecommenderName, currentRecommenderName)
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continue
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}
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vpaCRDs = append(vpaCRDs, vpaCRD)
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klog.V(3).Infof("Fetched %d VPAs.", len(vpaCRDs))
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// Add or update existing VPAs in the model.
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vpaKeys := make(map[model.VpaID]bool)
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…
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feeder.clusterState.ObservedVpas = vpaCRDs
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}
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```
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Accordingly, the VPA object yaml should include the `recommenderName` as the default `RecommenderName`.
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```yaml
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apiVersion: "autoscaling.k8s.io/v1"
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kind: VerticalPodAutoscaler
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metadata:
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name: hamster-vpa
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Spec:
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recommenderName: default
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targetRef:
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apiVersion: "apps/v1"
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... ...
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```
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### Deployment Details
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The customized recommender is supposed to be deployed as a separate deployment that is chosen
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by different sets of VPA objects.Each VPA object is supposed to choose only one recommender at a time.
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The way how the default recommender and the customized recommender are running and interacting with VPA objects
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are shown in the following drawing.
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<img src="images/deployment.png" alt="deployment" width="720" height="360"/>
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Though we do not support a VPA object to use multiple recommenders in this proposal, we leave the possibility of necessary
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changes of using multiple recommenders in the future. Namely, we define `recommenderName` to be an array instead of a string, but we support one element only in this proposal. We modify the admission controller to validate that the array has <= 1 elements.
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We will add the following check in the `func validateVPA(vpa *vpa_types.VerticalPodAutoscaler, isCreate bool)` function.
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```
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if len(vpa.Spec.RecommenderName) > 1 {
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return fmt.Errorf("VPA object shouldn't specify more than one recommenderNames.")
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}
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```
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## Design Details
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### Test Plan
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- Add e2e test demonstrating the default recommender ignores a VPA which specifies an alternate recommender.
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### Upgrade / Downgrade Strategy
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For cluster upgrades, the VPA from the previous version will continue working as before.
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There is no change in behavior or flags which have to be enabled or disabled.
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## Alternatives
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### Develop a plugin framework for customizable recommenders.
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Add a webhook system for customized recommendations. The default VPA recommender would
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call any available recommendation webhooks, and if any of them make a recommendation,
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the recommender would use that recommendation instead of making its own. If none make
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a recommendation, it would make its recommendation as it currently does. The plugin alternative
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is rejected because it involves much more design changes and code changes. It might be considered in the future if there are
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more use cases where running multiple recommenders for the same VPA object is needed.
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### Develop a label selector approach.
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Add a label for the CRD object to denote the recommender’s name. When making
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recommendations in the recommender, only the VpaCrds with the label
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`recommender=default` will be loaded and updated by the existing recommender.
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A label selector approach is rejected because it is too powerful and users can easily
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ignore those labels and misconfigure the VPA objects.
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## Out of Scope
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- Although this proposal will enable alternate recommenders, no alternate recommenders
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will be created as part of this proposal.
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- This proposal will not support running multiple recommenders for the same VPA object. Each VPA object
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is supposed to use only one recommender.
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## Implementation History
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<!--
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Major milestones in the lifecycle of a KEP should be tracked in this section.
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Major milestones might include:
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- the `Summary` and `Motivation` sections being merged, signaling SIG acceptance
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- the `Proposal` section being merged, signaling agreement on a proposed design
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- the date implementation started
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- the first Kubernetes release where an initial version of the KEP was available
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- the version of Kubernetes where the KEP graduated to general availability
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- when the KEP was retired or superseded
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-->
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