[GitHub] [incubator-yunikorn-core] wilfred-s commented on a change in pull request #106: [YUNIKORN-41] Update YuniKorn main README.md to make user can better get focus of the project

[GitBox <gi...@apache.org>]

wilfred-s commented on a change in pull request #106: [YUNIKORN-41] Update YuniKorn main README.md to make user can better get focus of the project
URL: https://github.com/apache/incubator-yunikorn-core/pull/106#discussion_r397777782
 
 

 ##########
 File path: README.md
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 @@ -40,9 +47,51 @@ Here are some key features of YuniKorn.
 - Customized resource types (like GPU) scheduling support.
 - Rich placement constraints support.
 - Automatically map incoming container requests to queues by policies. 
-- Node partition: partition cluster to sub-clusters with dedicated quota/ACL management. 
+- Node partition: partition cluster to sub-clusters with dedicated quota/ACL management.
+
+### Integration with K8s:
+
+The `k8shim` provides the integration for K8s scheduling and supported features include: 
+
+- _Predicates:_ All kinds of predicates such as node-selector, pod affinity/anti-affinity, taint/tolerant, etc.
+- _Persistent volumes:_ We have verified hostpath, EBS, NFS, etc. 
+- _K8s namespace awareness:_ YuniKorn support hierarchical of queues, does it mean you need to give up K8s namespace? Answer is no, with simple config, YuniKorn is able to 
+ support automatically map K8s namespaces to YuniKorn queues. All K8s-namespace-related ResourceQuota, permissions will be still valid.
+- _Metrics:_ Prometheus, Grafana integration.
+- _Cluster AutoScaler_: YuniKorn can nicely work with Cluster AutoScaler (https://github.com/kubernetes/autoscaler/tree/master/cluster-autoscaler) to drive cluster scales up and down.
+- _K8s Events_: YuniKorn also integrated with K8s events, so lots of information can be retrieved by using `kubectl describe pod`.
+
+#### Performance testing
+We love high-performance software, and we made tremendous efforts to make it to support large scale cluster and high-churning tasks. 
+Here's [Performance Test Result](docs/evaluate-perf-function-with-Kubemark.md) 
+
+#### Deployment model
+Yunikorn can be deployed as a K8s custom scheduler, and take over all POD scheduling. Community is actively working on 
+[Co-existing with other K8s schedulers](https://issues.apache.org/jira/browse/YUNIKORN-16) to allow YuniKorn take over subset of the cluster nodes. 
 
 Review comment:
   fixed in update with commit

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