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Container Orchestration and Management Systems Comparison from Technical View

Container Orchestration and Management Systems Comparison from Technical View

Container Orchestration and Management Systems Comparison from Technical View. This is the speak I did at KubeCon 2016

Lei (Harry) Zhang

June 28, 2017
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  1. Container Orchestration and Management Systems Comparison from Technical View Harry

    Zhang, Member of #CNCF
  2. The Scope of This Talk • Kubernetes • by Cloud

    Native Computing Foundation • Docker 1.12+ • by Docker Inc. • Compose + Swarm is kind of legacy, so they will not be included in this talk • Mesos • by Apache Software Foundation • only with Marathon, DC/OS is not included (the scope of later is larger)
  3. Chapter 1: Core Idea and Architecture

  4. Kubernetes • Build right things with containers by following concepts

    and conventions • like a “Spring Framework” in container eco-system • Design • master • api-server, scheduler, controller-manager • node • kubelet, kube-proxy • independent binaries • Pros: modular, transparent, manageable • Cons: a little bit complex to setup (1.4 is much better now) • network & volume plugins • driven by control loops
  5. kubelet SyncLoop kubelet SyncLoop proxy proxy 1 Pod created etcd

    scheduler api-server Kubernetes
  6. kubelet SyncLoop kubelet SyncLoop proxy proxy 2 Pod object added

    etcd scheduler api-server Kubernetes
  7. kubelet SyncLoop kubelet SyncLoop proxy proxy 3.1 New pod object

    detected 3.2 Bind pod with node etcd scheduler api-server Kubernetes
  8. kubelet SyncLoop kubelet SyncLoop proxy proxy 4.1 Detected pod bind

    with me 4.2 Start containers in pod etcd scheduler api-server Kubernetes
  9. kubelet SyncLoop controller-manager ControlLoop kubelet SyncLoop proxy proxy Objects: pod

    replica namespace service endpoint job deployment volume petset … etcd scheduler api-server Reconcile: desired world VS real world handler Kubernetes
  10. Tips: Control Theory* *Andrei, Neculai (2005). "Modern Control Theory –

    A historical Perspective" • It’s the basic model for: • Kubernetes controller and all other event loops • SwarmKit orchestrator • … ControlLoop
  11. Docker 1.12+ • Build-in cluster support for Docker containers •

    powered by swarmkit • SwarmtKit Design • build-in data store • manager • several components build into one binary • control loop driven • worker • use pull model to connect with manager WARNING: SwarmKit is currently a primitive project, expect change of this part
  12. Allocator Dispatcher Scheduler Orchestrator • API: accept commands from client

    • Create object in raft based memory store • github.com/coreos/etcd/raft for consensus • github.com/hashicorp/go-memdb for in-memory object storage • state, cluster, node, service, task, network … $ docker service create API Store SwarmKit Manager
  13. Allocator Dispatcher Scheduler • Create Tasks from Service object •

    Task: “start a container” etc • Reconcile loop for Service objects • Control Theory again Orchestrator API Store Orchestrator Service (replica=2) Task Task check if replica=2 or not SwarmKit Manager
  14. • Allocates IP addresses to Services and Tasks • (and

    allocate volumes in the future) • VIP and ports for Service • IP for all endpoints (veth pairs) in the network the task is attached to Orchestrator Dispatcher Scheduler API Store Allocator SwarmKit Manager Network Create
  15. • Assign Task to Node • unassignedTasks • nodeHeap •

    search in heap to find the best node which meets the constraints && has lightest workloads • ReadyFilter, ResourceFilter, ConstraintFilter Orchestrator Dispatcher API Store Scheduler Allocator SwarmKit Manager
  16. Manager • Nodes (agents) management • Dispatch assigned Task to

    corresponding Node Orchestrator API Store Allocator SwarmKit Manager Scheduler Dispatcher Dispatcher Agent Agent Agent grpc stream grpc stream grpc stream Task
  17. • Worker: • connect to Dispatcher to check assigned tasks

    • executor: execute tasks (containers) on this Node Worker Executor Agent Agent Adapter Docker Daemon docker.sock Worker Executor Worker Executor Agent
  18. Mesos 1.0 • A distributed systems kernel • originally designed

    to run big data job • core idea: fine-grained resource sharing • Mesos Design • Master + Slave + Zookeeper • two level scheduling • scheduler + executor = framework • need to use frameworks like Marathon for orchestration and management • containerizer • multiple container runtime & image support (>=1.0)
  19. MPI job MPI scheduler Hadoop job Hadoop scheduler Allocation module

    Mesos master Mesos slave MPI executor Mesos slave MPI executor task task Resource offer Pick framework to offer resources to *Animate: Operating Systems and Systems Programming Lecture 24 Anthony D. Joseph https://cs162.eecs.berkeley.edu/
  20. MPI job MPI scheduler Hadoop job Hadoop scheduler Allocation module

    Mesos master Mesos slave MPI executor Mesos slave MPI executor task task Pick framework to offer resources to Resource offer Resource offer = list of (node, availableResources) E.g. { (node1, <2 CPUs, 4 GB>), (node2, <3 CPUs, 2 GB>) } *Animate: Operating Systems and Systems Programming Lecture 24 Anthony D. Joseph https://cs162.eecs.berkeley.edu/
  21. MPI job MPI scheduler Hadoop job Hadoop scheduler Allocation module

    Mesos master Mesos slave MPI executor Hadoop executor Mesos slave MPI executor task task Pick framework to offer resources to task Framework-specific scheduling Resource offer Launches and isolates executors *Animate: Operating Systems and Systems Programming Lecture 24 Anthony D. Joseph https://cs162.eecs.berkeley.edu/
  22. How Docker plug into Mesos? • Before 1.0 • Docker

    Containerizer • Docker image -> task -> mesos-docker-executor -> Docker Daemon • Mesos 1.0 • Supporting multiple runtime & images • MesosContainerizer • “Mesos native container stack” • Isolators • Launcher Mesos slave Hadoop executor task mesos- docker- executor
  23. Checkpoint Kubernetes Docker SwarmKit Mesos+Marathon Design control loops driven control

    loops driven (but in single binary) two level scheduling Coordination etcd build-in raft Zookeeper Container Runtime multiple single, but has potential for more OCI runtimes multiple Container Image Docker Image, ACI, more in future Docker Image Docker Image, ACI, more in future Docker Daemon no need need no need
  24. About Build-In Data Store Pros Cons easy to setup hard

    to understand & debug fewer round trips hard to do backup/restore, migration, monitoring/audit easy to do performance tuning lack of mgmt API like:etcd admin guide
  25. Chapter 2: Control Panel

  26. Control Panel: Orchestration + Management • “Defines when and what

    to do next through out the automated workflow” • workload management • secret management • configuration management • scale and autoscaling • stateful workload • … and more
  27. Workload Management e.g. “a web server with 2 replicas” Kubernetes

    Docker SwarmKit Mesos+Marathon Description Deployment Service Application Version Control yes (revision) not yet yes (deployments)
  28. • Kubernetes “Deployment” • $ kubectl create -f <deployment-yaml> •

    $ kubectl edit <deployment> • this will open and edit object stored in etcd • update will trigger rolling update • $ kubectl set image <deployment> • $ kubectl scale —replicas=5 <deployment> … • $ kubectl rollout history <deployment> • $ kubectl rollout undo <deployment> —to-revision=<version> $ kubectl edit <deployment> …
  29. • Docker SwarmKit “Service” • $ docker service create SERVICE

    —replicas=5 … • $ docker service scale SERVICE=REPLICAS • $ docker service update [OPTIONS] SERVICE • rolling update • 30+ update options are supported • —container-label-add value • —container-label-rm value • --env-add value • --env-rm value • —image string • …
  30. • Mesos + Marathon “Application” • $ dcos marathon app

    start [--force] <app-id> [<instances>] • $ dcos marathon app update [--force] <app-id> [<properties>…] • rolling update • app dependencies are respected • $ dcos marathon app version list [--max-count=<max-count>] <app-id> … • $ dcos marathon deployment list [--json <app-id>] • $ dcos marathon deployment rollback <deployment-id>
  31. Secret Management • Kubernetes • Secret volume • encrypted and

    stored in etcd • consumed by ENV or volume • Docker SwarmKit • under discussion: https://github.com/docker/swarmkit/issues/1329 • Mesos + Marathon • only in DC/OS • stored in ZooKeeper, exposed as ENV in Marathon • Another similar feature is Configuration Management
  32. Configuration Management • Kubernetes • ConfigMap • stored in etcd,

    consumed by ENV or volume • $ kubectl create configmap example-redis-config —from-file=docs/redis-config • Docker SwarmKit • under discussion: https://github.com/docker/swarmkit/issues/1329 • Mesos + Marathon • not yet
  33. Autoscaling • Kubernetes • HorizontalPodAutoScaler • default: CPU • Custom

    Metrics: • user defined endpoint, e.g. http://localhost:9100/metrics • share same metric data structure with CNCF projects like Prometheus • Docker SwarmKit • not yet: https://github.com/docker/swarmkit/issues/486#issuecomment-219133613 • Mesos + Marathon • a stand-by `marathon-autoscale.py` • autoscales application based on the utilization metrics from Mesos
  34. Stateful Workload • Kubernetes • PetSet: Replicas with stable membership

    and volumes • stable hostname • ordinal index • stable storage • Docker SwarmKit • not yet, and don’t suggest stateful service • Mesos + Marathon • Stateful Applications • dynamic reservations, reservation labels, and persistent volumes. cassandra-0 volume 0 cassandra-0.cassandra.default.svc.cluster.local cassandra-1 volume 1 cassandra-1.cassandra.default.svc.cluster.local
  35. Chapter 3: Service Discovery & Load Balance

  36. Node Node Service Discovery & LB • Kubernetes • Load

    Balancer • iptables • External Access • <externalIP route to node(s)>:<port> • NodePort: <ip of any node>:<NodePort> • External LoadBalancer • Ingress (L7) • Ingress Pod: Nginx, HAproxy • SSL • Name Service • build-in skyDNS pod portal iptables rule 10.10.0.116:8001 random mode iptables rules Pod 2 Pod 1 ingress traffic http://foo.bar.com Node Ingress Pod internal traffic outside traffic pod rule 2 pod rule 1
  37. Worker Worker container sandbox ingress sandbox Service Discovery & Load

    Balance • Docker SwarmKit • Load Balancer • ipvs NAT mode • External Access • Routing Mesh • Name Service • embedded DNS server • for service and task Container 2 Container 1 ipvs Gossip to update the iptables & ipvs rules port mapping iptables iptables outside traffic (when service created with -p) internal traffic ipvs • Two kinds of sandboxes • ingress: on every worker • container: on workers where task lives • Two networks are needed • ingress overlay • user-defined overlay DNS: svc->vip ingress sandbox
  38. Service Discovery & Load Balance • Mesos + Marathon •

    Load Balancer • Marathon-lb: HAproxy based • virtual addresses (VIPs) in DC/OS • External Access • http://<public agent ip>:<servicePort> • external load balancer • Name Service • Mesos-DNS Slave Slave Marathon-lb Container 2 Container 1 Mesos-DNS
  39. Checkpoint Kubernetes Docker SwarmKit Mesos+Marathon Filter iptables VIP iptables VIP

    no need LB iptables random mode ipvs NAT mode HAproxy External Access nodeIP:port, Ingress, external IP/LB Routing Mesh (ingress overlay) same as expose HAproxy to public Update watch etcd gossip marathon_lb.py & template
  40. Chapter 4: Scheduling

  41. Kubernetes • Pod as schedule unit • this is unique,

    but why? • Multi-Scheduler • pod1: scheduler1, pod2 : scheduler2 • QoS tiers • anyone remember the core idea of Borg? • Guaranteed (requests == limit) • Burstable (requests < limit) • Best-Effort (no request & limit) • More Borg features are on the way • equivalence class, pod level resource boundary … Burstable Pod
  42. Docker SwarmKit • Task (container) as schedule unit • Multi-Scheduler

    • not yet • Strategy • pipeline of filters • ReadyFilter ResourceFilter ConstraintFilter • to sort nodeHeap • QoS tiers • not yet
  43. Mesos + Marathon • Task as schedule unit (Pod support

    in plan) • Multi-Scheduler • Mesos is designed to run multiple frameworks (schedulers) • Strategy • Two level scheduling (the killing weapon of Mesos) • Twitter scale … • fine-grained resource sharing (like Borg) • QoS tiers • of course • And much more • task eviction, data locality, max-min fairness, priority, offer reject, Delay Scheduling • and Big Data of course
  44. Chapter 5: Summary

  45. A Use Case: hyper.sh • hyper.sh is “Docker Done the

    Right Way” • $ hyper run mysql • $ hyper run --link mysql wordpress • $ hyper fip attach 22.33.44.55 wordpress • But Hyper.sh is powered by Kubernetes • and also maintain Kubernetes features
  46. Extensibility Really Matters • Hypernetes (h8s = k8s + HyperContainer)

    is what’s backing Hyper.sh: • HyperContainer runtime • Multi-tenant network based on Neutron • Custom Cinder plugin with Ceph backend • Custom HAproxy based Service • Kubernetes is truly extensible and configurable
  47. Just Personal Idea • So, if • I am a

    individual developer/org, trying to find something that is friendly and just works • I use Docker SwarmKit • I have a “Twitter scale” cluster to manage or I am a Big Data user • I need Mesos • But if what I need is a infrastructure layer to build my systems on top of it in right way • Kubernetes is the choice
  48. THE END @resouer