A Novel Flow Network Graph Based Scheduling Approach in K8S

Today’s Kubernetes clusters can comprise of 5,000-10,000 nodes, increase by an order of magnitude from original design specs. The default Kubernetes scheduler dates back from earlier more modest deployments. It is queue-based, processing unscheduled pods in a sequential fashion. For a large Kubernetes cluster, such scheduler processing logic can introduce long, even unbounded scheduling latencies, negatively affecting overall throughput for workload deployments. This talk outlines how Kubernetes is a victim of its own success, scheduling-wise. It lays out the current pod queuing challenges and describes a novel scheduling approach based on Flow Network Graph technique, enabling low workload scheduling latencies at scale.
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1.A Novel Flow Network Graph Based Scheduling Approach in Kubernetes @kevin-wangzefeng wangzefeng@huawei.com

2.Agenda • Scheduling in K8S • The Default Scheduler • Firmament & Poseidon • Future plans

3.Scheduling in Kubernetes apiVersion: v1 kind: Pod metadata: labels: run: my-pod name: my-pod-76559f5d5b-l9b9p namespace: default ...... spec: dnsPolicy: ClusterFirst nodeName: node1 restartPolicy: Always schedulerName: default-scheduler containers: ......

4. The default scheduler • Queue based – one pod one time – best fit (scheduling time) • Resource allocation model – Request based, not real-time usage – Low utilization (due to uncertain user resource estimation) • Policies implemented as two sets of algorithms: – Predicates – Priorities

5.What is Firmament • Flow based scheduler – Models workloads and cluster as a flow network (DAG) – Policies considered at DAG build / update – Run Min-Cost Max-Flow (MCMF) solver to find an optimal flow – Scheduling results extracted from the optimal flow

6.diff Firmament Kube-scheduler • Similar to default scheduler – “Global optimal solution” – Pluggable scheduling policies • That makes differences – Flexible resource modeling, easy to extend to support topology (zones, racks, NUMA, etc.) – Built-in support with rescheduling, priority and preemption – And a set of other cost models: • network-ware, Quincy, load-spreading etc. – Low decision latency at scale • sub-second decisions at 10k+ machines – batching approach – By default use resource utilization instead of reservation

7.Flow network example in Firmament • Flow network - 4 machine cluster, 2 jobs (3 tasks and 2 tasks). • Arc labels show non-zero costs - (values depends on policies.) • All arcs have unit capacity - except those between unscheduled aggregators and the sink. • The red arcs carry flow and form the min-cost solution. - All tasks except T0,1 are scheduled on machines.

8.And Poseidon? To fill the gaps between K8S and Kubernetes Heapster Firmament API server Poseidon sink • Different concepts s at st Node/pod n bindings – K8S: workloads, pods events tio Pod
 z a ili 
 – Firmament: jobs, tasks ut • Different language Utilization stats gRPC service – K8S: Golang – Firmament: C++ Poseidon Scheduling
 Utilization Node/task decisions • Resource Requests v.s. Real- events stats 
 
 time utilization – K8S: allocate by requests and “un- claimed” Firmament
 – Firmament: utilization statistics gRPC service

9.Poseidon Design Kubernetes API server Heapster Utilization events events bindings Node stats Pod Pod Poseidon Node watcher Pod watcher Utilization Scheduler loop statistics gRPC Node keyed Pod keyed
 service queue queue Node worker pool Pod worker pool Scheduling Utilization decisions Schedule events events tasks Node stats Task Firmament gRPC service

10.Flow Network aligned to Kubernetes Concept Node Affinit P0 y 2 20 N0 7 R0 P1 3 N1 P2 5 C S N2 ty ini R1 P3 A f f 5 ode N3 N 9 P4 U 15

11.Status and Progress • Incubating under K8S scheduler SIG – https://github.com/kubernetes-sigs/poseidon – Currently Alpha (v0.3) – Support CPU/Memory Cost model – Node Affinity/Anti-Affinity – Pod Affinity/Anti-Affinity – Automation for E2E tests, PR process etc. – and more…

12.30X algorithmic throughput No Nodes Pods Poseidon Default Scheduler 1 200 3800 26027 761 2 400 7600 15200 361 3 600 11400 12351 265

13.Future plans • Under development – Max allowed pods for nodes. – Taints & Tolerations. – Another round of benchmarking for scalabilities, performances. • Longer future: – Transitioning to Metrics server API (Heapster is going to be deprecated). – High Availability / Failover for in-memory Firmament/Poseidon processes. – Priority Pre-emption support. – Gang Scheduling. – Resource Utilization benchmark. – Better cooperating with the default scheduler. (enhancements on multi-scheduler framework) – Checkout https://github.com/kubernetes-sigs/poseidon/issues for more…

14.Join us! • Scheduling SIG – https://groups.google.com/forum/#!forum/kubernetes-sig- scheduling • Poseidon Project – https://github.com/kubernetes-sigs/poseidon • Follow Huawei Container team 
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15.Thank you!

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