7 The component to literally schedule each Pod to Node. Checks many factors (resources, affinity, volume, etc) and decides the best Node for the Pod. Kubernetes scheduler
9 Control your scheduling Category description Built-in scheduling constraints The scheduling constraints on Pod spec Control the scheduling per Pod. KubeSchedulerConfiguration Control the scheduling per cluster. Extend the scheduler Extender Via webhook Plugin Via your own scheduler plugin
10 Control your scheduling Category description Built-in scheduling constraints The scheduling constraints on Pod spec Control the scheduling per Pod. KubeSchedulerConfiguration Control the scheduling per cluster. Extend the scheduler Extender Via webhook Plugin Via your own scheduler plugin
14 Control your scheduling Category description Built-in scheduling constraints The scheduling constraints on Pod spec Control the scheduling per Pod. KubeSchedulerConfiguration Control the scheduling per cluster. Extend the scheduler Extender Via webhook Plugin Via your own scheduler plugin
15 Control your scheduling Category description Built-in scheduling constraints The scheduling constraints on Pod spec Control the scheduling per Pod. KubeSchedulerConfiguration Control the scheduling per cluster. Extend the scheduler Extender Via webhook Plugin Via your own scheduler plugin
16 Control your scheduling Category description Built-in scheduling constraints The scheduling constraints on Pod spec Control the scheduling per Pod. KubeSchedulerConfiguration Control the scheduling per cluster. Extend the scheduler Extender Via webhook Plugin Via your own scheduler plugin WebAssembly Via WebAssembly plugin
17 Unique use cases sometimes require extending your scheduler. ● The batch jobs requirements. ○ Start several Pods at the same time (coscheduling) ○ Elastic resource quota (capacityscheduling) ● You’ll find many other usecases in kubernetes-sigs/scheduler-plugins Extend your scheduler
18 Control your scheduling Category description Built-in scheduling constraints The scheduling constraints on Pod spec Control the scheduling per Pod. KubeSchedulerConfiguration Control the scheduling per cluster. Extend the scheduler Extender Via webhook Plugin Via your own scheduler plugin WebAssembly Via WebAssembly plugin
19 Webhook based extension on the scheduler. Each webhooks are called at a specific point in scheduling: ● Filter: Filter in Scheduling Framework ● Prioritize: Score in Scheduling Framework ● Preempt: PostFilter in Scheduling Framework ● Bind: Bind in Scheduling Framework Extender
20 ● 👍 No need to rebuild scheduler. (Just pass URL via config) ● 👍 The flexibility of implementation. ● 👎 It affects the scheduling latency very badly. ● (👎 See this for more disadvantages.) Extender
23 Scheduling Framework: the pluggable architecture of scheduler. ● Decouple all scheduling logic from the scheduler’s core impl ● One scheduling factor = One plugin ● We can extend the scheduler by creating your own plugins. Plugin (Scheduling framework)
29 ● 👍 More extension points are available. ● 👍 No overhead to call plugins. ● 👎 Cannot use it casually. (requires rebuild etc) Plugin (Scheduling framework)
30 ● 👍 More extension points are available. ● 👍 No overhead to call plugins. ● 👎 Cannot use it casually. (requires rebuild etc) Plugin (Scheduling framework)
31 ● Maintenance cost ○ Need to fork the scheduler and keep consistent with your Kubernetes version. ● The scheduler should be only one in the cluster. ○ Need to let all Pods go through a new scheduler in some ways. ○ May need to convince people managing the scheduler in your cluster. (your infra team, cloud vendor, etc) ○ May need to maintain multiple scheduling plugins owned by different teams in the scheduler. Hurdles for Plugin extension
32 ● Maintenance cost ○ Need to fork the scheduler and keep consistent with your Kubernetes version. ● The scheduler should be only one in the cluster. ○ Need to let all Pods go through a new scheduler in some ways. ○ May need to convince people managing the scheduler in your cluster. (your infra team, cloud vendor, etc) ○ May need to maintain multiple scheduling plugins owned by different teams in the scheduler. Can we call it a true pluggable system? 🤔 Hurdles for Plugin extension
34 WebAssembly is a way to safely run code compiled in other languages. ● Wasm runtimes execute wasm guests (xxxx.wasm) ● Wasm guests import functions from host. ○ = They cannot do other things. You may hear it around the browser stuff, but… WebAssembly
38 It’s very tough for non-wasm people to write own wasm guest to satisfy ABIs. We’re providing TinyGo SDK so that people can develop wasm plugins via a similar experience with Golang native plugins. – just need to implement interfaces. TinyGo SDK
42 The wasm plugins are portable, easy to distribute from the communities, and easy to apply to your scheduler. Add your wasm plugin Wow, it can use http(s)!
43 It's a balanced solution between the extender and golang plugin. ● 👍 All extension points are available. ● 👍 No need to change the scheduler’s code, no need to rebuild! ● 👍 Easy to distribute plugins. (via http(s)) ● 👍 It can be written in many languages. ● 👎 A bad impact on the latency. ● 👎 Wasm peculiar limitations. So… will wasm extension replace all plugins?
44 It's a balanced solution between the extender and golang plugin. ● 👍 All extension points are available. ● 👍 No need to change the scheduler’s code, no need to rebuild! ● 👍 Easy to distribute plugins. (via http(s)) ● 👍 It can be written in many languages. ● 👎 A bad impact on the latency. ● 👎 Wasm peculiar limitations. So… will wasm extension replace all plugins?
48 Golang plugin can still be the best if… ● The scheduler’s latency is super critical in your cluster. ○ The bigger cluster you get, the faster scheduling is needed. ● You need to do heavy-calculation or handle tons of various objects. ○ Due to inlined GC and the latency to pass objects from host to guest. (both will be discussed in later section) So… will wasm extension replace all plugins?
53 WebAssembly’s sandbox model and its limitation: ● The guest can only operate their memory. ● The guest memory is exported to host so that host can read or write anything. ● Only numeric types are supported. The wasm function can only operate their memory
54 Example ABI to get URI from host. (http-wasm.io) ● Guest: allocates enough memory for URI and gives the linear memory offset and maximum length in bytes. ● Host: put the URI there and tell guest the length of it. How to pass things from host
56 One function to fetch protobuf encoded Pod, Node, etc. For the performance concern, we do ● Lazy loading ● Cache ● Faster garbage collection ● (Other future planned enhancements) Protobuf encoding
58 The scheduler refers the same resource status in one scheduling cycle. → Why don’t we have a cache in wasm guest! We reduce the number of communication as much as possible with them: ● Fetch objects from host only when it’s necessary for the guest. ● Fetch the same object from host only once during one scheduling cycle. Lazy loading with caching
61 The performance is the critical factor for the project. We have two levels of benchmark test: ● Plugin-level benchmark tests, using the Golang benchmark test feature. ○ How much it takes time in which part. ● The scheduler_perf, running the wasm plugin in the scheduler and observing the scheduler’s metrics. ○ How much actually the wasm slows down the scheduling. Benchmark tests
63 It’s an early stage, but it’s already have an enough functionality to write a simple wasm plugin! See examples if you’re interested in! Project status
64 ● Support all extension points. ● Get resources other than Pods and Nodes. ● Further performance improvement. ● Other language examples for guest. What’s left to do
65 ● Join us #sig-scheduling on Kubernetes slack! ● Shout out to all contributors so far, especially Adrian for tons of contributions, and all people, especially Chris for all helps to bring me here, Wellington . We’re running through many things in 30 min 🏃💨, Thanks all! That’s all!
73 The scheduler needs to consider many things: ● Resource request on Pod. ● Affinity requirement on Pod. (PodAffinity, NodeAffinity) ● How Pods are spread into each domain now. (PodTopologySpread) ● Taints on Nodes / Tolerations on Pod. ● …etc Scheduling factors
74 The architecture inside the scheduler ● The scheduler is composed of many Plugins. ○ One scheduling factor = one plugin (NodeAffinity plugin, etc) ● Each plugin is created to work on one or more extension points. ○ Filter: filtering Nodes that don’t fit the requirements ○ Score: scoring the remaining Nodes ○ … Scheduling Framework
78 Role: Filtering out Nodes that shouldn’t/cannot run the Pod For example.. ● Nodes that don’t have enough resource to run the Pod ● Nodes that don’t match with a required NodeAffinity on the Pod ● … Scheduling Framework - Filter
80 Role: Scoring each Node that passed all Filter plugins For example, give higher scores to Nodes ● already have the container image(s) of the Pod ● match with a preferred NodeAffinity on Pod ● … Scheduling Framework - Score
82 The cluster level scheduling configuration: ● Disable/Enable plugins in the scheduler ● Change plugins’ behaviors of all scheduling KubeScheduler Configuration
85 We explored Golang standard package named plugin, but gave up. You can see our investigation and discussion here: ● https://github.com/kubernetes/kubernetes/issues/106705 ● https://github.com/kubernetes/kubernetes/issues/100723 Several attempts to make it easier
86 People start to use Wasm to provide the extendability – load and run the wasm binary in the host. In Golang, there are already some: - Dapr Wasm middleware - Trivy Modules - knqyf263/go-plugin Powered by WebAssembly with Golang
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