{"action":"created","comment":{"url":"https://api.github.com/repos/kubernetes/kubernetes/pulls/comments/42718280","id":42718280,"diff_hunk":"@@ -0,0 +1,269 @@\n+<!-- BEGIN MUNGE: UNVERSIONED_WARNING -->\n+\n+<!-- BEGIN STRIP_FOR_RELEASE -->\n+\n+<img src=\"http://kubernetes.io/img/warning.png\" alt=\"WARNING\"\n+     width=\"25\" height=\"25\">\n+<img src=\"http://kubernetes.io/img/warning.png\" alt=\"WARNING\"\n+     width=\"25\" height=\"25\">\n+<img src=\"http://kubernetes.io/img/warning.png\" alt=\"WARNING\"\n+     width=\"25\" height=\"25\">\n+<img src=\"http://kubernetes.io/img/warning.png\" alt=\"WARNING\"\n+     width=\"25\" height=\"25\">\n+<img src=\"http://kubernetes.io/img/warning.png\" alt=\"WARNING\"\n+     width=\"25\" height=\"25\">\n+\n+<h2>PLEASE NOTE: This document applies to the HEAD of the source tree</h2>\n+\n+If you are using a released version of Kubernetes, you should\n+refer to the docs that go with that version.\n+\n+<strong>\n+The latest 1.0.x release of this document can be found\n+[here](http://releases.k8s.io/release-1.0/docs/proposals/cluster-autoscaling.md).\n+\n+Documentation for other releases can be found at\n+[releases.k8s.io](http://releases.k8s.io).\n+</strong>\n+--\n+\n+<!-- END STRIP_FOR_RELEASE -->\n+\n+<!-- END MUNGE: UNVERSIONED_WARNING -->\n+\n+# Cluster Autoscaling\n+\n+## Preface\n+\n+This document briefly describes the design and roadmap of cluster autoscaling in Kubernetes.\n+\n+## Overview\n+\n+The purpose of Cluster Autoscaler is to adjust the size of the Kubernetes cluster to match\n+the current needs of the applications running inside of the cluster. In general, if cluster\n+nodes are under heavy load or there are pods that cannot be scheduled due to insufficient\n+free resources then more machines should be added to the cluster. In a similar way if the\n+utilization on the machines is low and it is apparent that the same set of pods could be run on\n+fewer nodes, then the size of the cluster should be reduced.\n+\n+## Kubernetes 1.1 - no Kubernetes-native cluster autoscaling\n+\n+In the current Kubernetes version we have a basic support for cluster autoscaling. It is based\n+on Google Cloud Autoscaler (https://cloud.google.com/compute/docs/autoscaler/) and works only\n+when Kubernetes is running on Google Compute Engine. It looks at total cpu usage on each node (including system stuff)\n+and total cpu request of all running pods. It can also scale on memory but for the simplicity\n+this document will focus only on the CPU usage aspect (other metrics work in the exactly same way).\n+The user is expected to provide the target level of utilization (which is now common for all metrics)\n+and GCA will adjust the cluster size to meet the target. From the bird’s eye the architecture looks as below.\n+\n+```\n+                  +--MIG-----------------------------+\n+                  |                                  |\n+                  | +--------+ +--------+ +--------+ |\n+                  | |        | |        | |        | |\n+                  | | Node1  | | Node2  | | NodeX  | |\n+                  | |        | |        | |        | +<--+\n+                  | +--------+ +--------+ +--------+ |   |\n+                  | |cAdvisor| |cAdvisor| |cAdvisor| |   |\n+                  | +--------+ +--------+ +----+---+ |   |\n+                  |     |          |           |     |   |\n+                  +-----|----------|-----------|-----+   |\n+                        |          |           |         |\n+                        |      +---v----+      |         |\n+                        +----> |Heapster| <----+         |\n+                               +---+----+                |\n+                                   |                     |\n+   Kubernetes                      |                     |\n+-----------------------------------|---------------------|------\n+   Google                          |                     |\n+                                   |                     |\n+                            +------v-----+       +-------+------+\n+                            |Google Cloud|       |MIG Definition|\n+                            | Monitoring |       +-------^------+\n+                            +------------+               |\n+                                   |                     |\n+                                   |                     |\n+                            +------v-----+               |\n+                +------+    |Google Cloud+---------------+\n+                |Config|--->| Autoscaler |\n+                +------+    +------------+\n+```\n+\n+CAdvisor instances gather metrics for nodes they are running on. Periodically Heapster (a service running\n+on one of the nodes) queries all of the available nodes/cAdvisors, aggregates the metrics and pushes the\n+usage to Google Cloud Monitoring (Heapster also takes Pod requests from apiserver but this fact is not\n+depicted on the diagram). Google Cloud Autoscaler checks the metrics in GCM, compares the values\n+and min/max cluster sizes from the config (that is passed through google-specific gcloud tool or web ui,\n+and currently set up in kube-up/push) and updates the number of nodes in MIG (Managed Instance Group)\n+definition. MIG then starts (or stops) the nodes.\n+\n+\n+The current solution has a bunch of issues:\n+\n+* (I1) [GCE-specific API] Autoscaler configuration is Google-specific thus on other cloud providers the configuration may look completely different.\n+* (I2) [GCE-specific logic] Autoscaler logic (scale on a custom metric) is Google-specific - it might be hard to get it working on AWS or elsewhere\n+because their autoscaler either does not allow to use custom metric or interprets/acts differently.\n+* (I3) [Works well only for well-formed pods]\n+Autoscaler grows the cluster only in reaction to resource consumption of pods that are already running, i.e.\n+it does not take into account pods that are pending. It still tends to increase the probability that\n+a pod will schedule, by keeping the cluster \"not too full\" (reasonable amount of free resources), but\n+it does not guarantee to prevent pods that stay pending indefinitely - for example, the resources might be\n+too fragmented to meet the pod's requirements, or the pod might have a node selector that can't be satisfied\n+by any of the nodes that have sufficient free resources. More specifically on the first point, cluster autoscaler guarantees\n+scheduling only the pods that are smaller than `1-target` cpu-request-utilization. If the target is `75% (* machine-cpu-count)`\n+ then only pods of size `25% * machine-cpu-count` will schedule (assuming that they don’t have other serious constraints).\n+Bigger pods scheduling is best-effort - autoscaler may keep it unscheduled forever. (see small tasks in appendix at the end of this document)\n+* (I4) [Autoscaler kills nodes at random when scaling down] K8S does not influence which node is killed when scaling down.\n+* (I5) [Hard to debug] Autoscaler decisions are not propagated back to Kubernetes as events.\n+* (I6) [Hard to enable/disable] Autoscaler setup takes place on cluster startup.\n+* (I7) [Heapster won't schedule] All scaling decisions require Heapster to be up and running. In case of a very\n+unfortunate scale down (I4) combined with new tasks it may be possible that Heapster will not\n+start again leaving the cluster in the fixed, too small size.\n+\n+## Kuberntes 1.2 - small KAC\n+\n+For Kubernetes 1.2 we plan to modify the architecture slightly. The main difference is moving the config to the Kubernetes world.\n+The config will be placed in apiserver (or in the other config storage that will be ready for 1.2)  and will be handled by\n+kubernetes-specific tools (kubectl or so). A new kubernetes controller will be added - Kubernetes Autoscaler for Cluster (abbreviated as KAC).\n+\n+In version 1.2 we will provide “small” KAC. Its responsibility will be mainly limited to reading the config and updating Google\n+Cloud Autoscaler accordingly. It will also observe changes in MIG and generate appropriate kubernetes events.\n+\n+```\n+                          +--MIG-----------------------------+\n+                          |                                  |\n+                          | +--------+ +--------+ +--------+ |\n+                          | |        | |        | |        | |\n+                          | | Node1  | | Node2  | | NodeX  | |\n+                          | |        | |        | |        | +<--+\n+                          | +--------+ +--------+ +--------+ |   |\n+            +---------+   | |cAdvisor| |cAdvisor| |cAdvisor| |   |\n+            |  Config |   | +--------+ +--------+ +----+---+ |   |\n+            +---------+   |     |          |           |     |   |\n+                 |        +-----|----------|-----------|-----+   |\n+                 |              |          |           |         |\n+            +----v----+         |      +---v----+      |         |\n+            |   KAC   |         +----> |Heapster| <----+         |\n+            +---------+                +---+----+                |\n+                 |                         |                     |\n+ Kubernetes      |                         |                     |\n+-----------------|-------------------------|---------------------|------\n+  Google         |                         |                     |\n+                 |                         |                     |\n+                 |                  +------v-----+       +-------+------+\n+                 |                  |Google Cloud|       |MIG Definition|\n+                 |                  | Monitoring |       +-------^------+\n+                 |                  +------------+               |\n+                 |                         |                     |\n+                 |                         |                     |\n+                 |                  +------v-----+               |\n+                 +----------------->|Google Cloud+---------------+\n+                                    | Autoscaler |\n+                                    +------------+\n+```\n+\n+\n+We will also configure GCE Autoscaler so that it deletes nodes  in a non-random way. There are couple strategies for killing to be considered:\n+\n+* Node with the fewest number of pods.\n+* Node with the fewest number of non-replicated pods.\n+* Node with the lowest load.\n+* Node with fewest number of guaranteed-QoS pods.\n+\n+GCA will know about our preferences from the metrics that would go to GCM the similar way as cpu usage (it will kill nodes starting from\n+the one with the lowest metric). As for 1.2 the corresponding API (for selecting which metric to use to choose the victim)\n+may not be ready we will use a predefined metric name and suffix for GCA name.\n+We will use MIG shutdown script to support pod graceful termination in case of node deletion.\n+\n+This will solve the following problems:\n+\n+* (I1) [GCE specific API] we will have a single tool for all autoscalers (kubectl) with defined configuration\n+* (I4) [Autoscaler kills nodes at random] We will use simplest metric (number of pods per node); based on user\n+feedback we may enhance it and include more advanced logic by computing per node metrics inside KAC (to take\n+into account what pods are running on each node). It will also help greatly with (I7).\n+* (I5) [Hard to debug] KAC will generate events that correspond to GCA decisions based on gcloud compute operations list.\n+* (I6) [Hard to enable/disable] user can easily update the configuration on a running cluster.\n+\n+It will also allow us to switch the implementation later without changing the interface (too much).\n+\n+This will not solve the following problems:\n+\n+(I2) [GCE specific logic] To port this solution to other cloud providers one would have to reimplement whole logic that\n+implements common interface (or configure cloud-provider specific autoscaler); it’s possible but hard.\n+(I3) [Works only for well formed pods] We will also introduce an experimental/hacky metrics for pending pods that will be useful\n+for scaling up clusters when the pods are larger than 1-target. This will be only a help, not a full solution.\n+\n+For development/experimentation/proof-of-concept KAC config it will be stored in Etcd as an API object. 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