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Kubernetes Walk-through
by Harry Zhang @resouer
Kubernetes
• Created by Google Borg/Omega team
• Founded and operated by CNCF (Linux Foundation)
• Container orchestration, scheduling and management
• One of the most popular open source project in the world
Project State
Growing Contributors
• 1728+ authors
Architecture
kubelet
SyncLoop
controller-manager
ControlLoop
kubelet
SyncLoop
proxy
proxy
network
pod
replica
namespace
service
job
deployment
volume
petset
…
scheduler
Node
Node
Desired World
Real World
etcdapi-server
Example
kubelet
SyncLoop
kubelet
SyncLoop
proxy
proxy
1 Container created
etcd
scheduler
api-server
Example
kubelet
SyncLoop
kubelet
SyncLoop
proxy
proxy
2 Object added
etcd
scheduler
api-server
Example
kubelet
SyncLoop
kubelet
SyncLoop
proxy
proxy
3.1 New container detected
3.2 Bind container to a node
etcd
scheduler
api-server
Example
kubelet
SyncLoop
kubelet
SyncLoop
proxy
proxy
4.1 Detected bind operation
4.2 Start container on this machine
etcd
scheduler
api-server
Take Aways
• Independent control loops
• loosely coupled
• high performance
• easy to customize and extend
• “Watch” object change
• Decide next step based on state change
• not edge driven (event), level driven (state)
{Pod} = a group of containers
Co-scheduling
• Tow containers:
• App: generate log files
• LogCollector: read and redirect logs to storage
• Request MEM:
• App: 1G
• LogCollector: 0.5G
• Available MEM:
• Node_A: 1.25G
• Node_B: 2G
• What happens if App is scheduled to Node_A first?
Pod
• Deeply coupled containers
• Atomic scheduling/placement unit
• Shared namespace
• network, IPC etc
• Shared volume
• Process group in container cloud
Why co-scheduling?
• It’s about using container in right way:
• Lesson learnt from Borg: “workloads tend to have tight relationship”
Ensure Container Order
• Decouple web server
and application
• war file container
• tomcat container
• Wrong!
Multiple Apps in One Container?
Master Pod
kube-apiserver
kube-scheduler
controller-manager
⽇日志看不不到

是否running没法判断

运维操作困难

出错定位麻烦,不不知道是哪个挂了了,频繁登陆容器器
Copy Files from One to Another?
• Wrong!
Master Pod
kube-apiserver
kube-scheduler
controller-manager
/etc/kubernetes/ssl
Connect to Peer Container thru IP?
• Wrong!
Master Pod
kube-apiserver
kube-scheduler
controller-manager
network namespace
So this is Pod
• Design pattern in container world
• decoupling
• reuse & refactoring
• Describe more real-world workloads by container
• e.g. ML
• Parameter server and trainer in same Pod
Kubernetes Control Panel
1. How Kubernetes schedule
workloads?
Resource Model
• Compressible resources
•  Hold no state
•  Can be taken away very quickly
•  “Merely” cause slowness when revoked
•  e.g. CPU
• Non-compressible resources
• Hold state
• Are slower to be taken away
• Can fail to be revoked
• e.g. Memory, disk space
Kubernetes (and Docker) can only handle CPU & Memory
Don’t handle things like memory bandwidth, disk time,
cache, network bandwidth, ... (yet)
Resource Model
• Request: amount of a resource allowed
to be used, with a strong guarantee of
availability
•  CPU (seconds/second), RAM (bytes)
•  Scheduler will not over-commit
requests
• Limit: max amount of a resource that
can be used, regardless of guarantees
• scheduler ignores limits
• Mapping to Docker
• —cpu-shares=requests.cpu
• —cpu-quota=limits.cpu
• —cpu-period=100ms
• —memory=limits.memory
QoS Tiers and Eviction
• Guaranteed
• limits is set for all resources, all containers
• limits == requests (if set)
• Be killed until they exceed their limits
• or if the system is under memory pressure and there are no lower priority containers that can be killed.
• Burstable
• requests is set for one or more resources, one or more containers
• limits (if set) != requests
• killed once they exceed their requests and no Best-Effort pods exist when system under memory pressure
• Best-Effort
• requests and limits are not set for all of the resources, all containers
• First to get killed if the system runs out of memory
Scheduler
• Predicates
• NoDiskConflict
• NoVolumeZoneConflict
• PodFitsResources
• PodFitsHostPorts
• MatchNodeSelector
• MaxEBSVolumeCount
• MaxGCEPDVolumeCount
• CheckNodeMemoryPressure
• eviction, QoS tiers
• CheckNodeDiskPressure
• Priorities
• LeastRequestedPriority
• BalancedResourceAllocation
• SelectorSpreadPriority
• CalculateAntiAffinityPriority
• ImageLocalityPriority
• NodeAffinityPriority
• Design tips:
• watch and sync podQueue
• schedule based on cached info
• optimistically bind
• predicates is paralleled between
nodes
• priorities are paralleled between
functions in Map-Reduce way
Multi-Scheduler
The 2nd scheduler
• Tips: annotation: system usage labels
• Do NOT abuse labels
2. Workload management?
Deployment
• Replicas with control
• Bring up a Replica Set and Pods.
• Check the status of a Deployment.
• Update that Deployment (e.g. new image, labels).
• Rollback to an earlier Deployment revision.
• Pause and resume a Deployment.
Create
• ReplicaSet
• Next generation of ReplicaController
• —record: record command in the annotation of ‘nginx-deployment’
Check
• DESIRED: .spec.replicas
• CURRENT: .status.replicas
• UP-TO-DATE: contains the latest pod template
• AVAILABLE: pod status is ready (running)
Update
• kubectl set image
• will change container image
• kubectl edit
• open an editor and modify
your deployment yaml
• RollingUpdateStrategy
• 1 max unavailable
• 1 max surge
• can also be percentage
• Does not kill old Pods until a sufficient
number of new Pods have come up
• Does not create new Pods until a
sufficient number of old Pods have
been killed.
trigger
Update Process
• The update process is coordinated by Deployment
Controller
• Create: Replica Set (nginx-deployment-2035384211) and scaled it up to 3 replicas directly.
• Update:
• created a new Replica Set (nginx-deployment-1564180365) and scaled it up to 1
• scaled down the old Replica Set to 2
• continued scaling up and down the new and the old Replica Set, with the same rolling update
strategy.
• Finally, 3 available replicas in the new Replica Set, and the old Replica Set is scaled down to 0.
Rolling Back
• Check reversions
• Roll back to reversion
Pausing & Resuming
(Canary)
• Tips
• blue-green deployment: duplicated infrastructure
• canary release: share same infrastructure
• rollback resumed deployment is WIP
• old way: kubectl rolling-update rc-1 rc-2
3. Deploy Daemon workload to
every Node?
DaemonSet
• Spread daemon pod to every node
• DaemonSet Controller
• bypass default scheduler
• even on unschedulable nodes
• e.g. bootstrap
4. Automatically scale?
Horizontal Pod Autoscaling
• Tips
• Scale out/in
• TriggeredScaleUp (GCE, AWS, will add more)
• Support for custom metrics
Custom Metrics
• Endpoint (Location to collect metrics from)
• Name of metric
• Type (Counter, Gauge, ...)
• Data Type (int, float)
• Units (kbps, seconds, count)
• Polling Frequency
• Regexps (Regular expressions to specify
which metrics to collect and how to parse
them)
• The metric will be added to pod as
ConfigMap volume
Prometheus
Nginx
5. Pass information to workloads?
ConfigMap
• Decouple configuration from image
• configuration is a runtime attribute
• Can be consumed by pods thru:
• env
• volumes
ConfigMap Volume
• No need to use Persistent Volume
• Think about Etcd
Secret
• Tip: credentials for
accessing the k8s API is
automatically added to
your pods as secret
6. Read information from system
itself?
Downward Api
• Get these inside your pod as
ENV or volume
• The pod’s name
• The pod’s namespace
• The pod’s IP
• A container’s cpu limit
• A container’s cpu request
• A container’s memory limit
• A container’s memory request
7. Service discovery?
Service
• The unified portal of replica Pods
• Portal IP:Port
• External load balancer
• GCE
• AWS
• HAproxy
• Nginx
• OpenStack LB
Service Implementation
Tip: ipvs solution works in nat mode which is the same with this iptables way
$ iptables-save | grep my-service
-A KUBE-SERVICES -d 10.0.0.116/32 -p tcp -m comment --comment "default/my-service: cluster IP" -m tcp --dport 8001 -j KUBE-SVC-KEAUNL7HVWWSEZA6
-A KUBE-SVC-KEAUNL7HVWWSEZA6 -m comment --comment "default/my-service:" --mode random -j KUBE-SEP-6XXFWO3KTRMPKCHZ
-A KUBE-SVC-KEAUNL7HVWWSEZA6 -m comment --comment "default/my-service:" --mode random -j KUBE-SEP-57KPRZ3JQVENLNBRZ
-A KUBE-SEP-6XXFWO3KTRMPKCHZ -p tcp -m comment --comment "default/my-service:" -m tcp -j DNAT --to-destination 172.17.0.2:80
-A KUBE-SEP-57KPRZ3JQVENLNBRZ -p tcp -m comment --comment "default/my-service:" -m tcp -j DNAT --to-destination 172.17.0.3:80
Publishing Services
• Use Service.Type=NodePort
• <node_ip>:<node_port>
• External IP
• IPs route to one or more cluster nodes (e.g. floating IP)
• Use external LoadBalancer
• Require support from IaaS (GCE, AWS, OpenStack)
• Deploy a service-loadbalancer (e.g. HAproxy)
• Official guide: https://github.com/kubernetes/contrib/tree/master/service-loadbalancer
Ingress
• The next generation external Service load
balancer
• Deployed as a Pod on dedicated Node
(with external network)
• Implementation
• Nginx, HAproxy, GCE L7
• External access for service
• SSL support for service
• …
s1http://foo.bar.com <IP_of_Ingress_node>
http://foo.bar.com/foo
Headless Service
*.nginx.default.svc.cluster.local
app=nginx app=nginx app=nginx
also: subdomain
8. Stateful applications?
StatefulSet: “clustered applications”
• Ordinal index
• startup/teardown ordering
• Stable hostname
• Stable storage
• linked to the ordinal & hostname
• Databases like MySQL or PostgreSQL
• single instance attached to a persistent volume at any time
• Clustered software like Zookeeper, Etcd, or Elasticsearch, Cassandra
• stable membership.
Update StatefulSet:
Scale: create/delete one by one
Scale in: will not delete old persistent volume
StatefulSet
StatefulSet Example
cassandra-0 cassandra-1
volume 0 volume 1
cassandra-0.cassandra.default.svc.cluster.local
cassandra-1.cassandra.default.svc.cluster.local
$ kubectl patch petset cassandra -p '{"spec":{"replicas":10}}'
9. Container network?
One Pod One IP
• Network sharing is important for affiliate
containers
• Not all containers need independent
network
• Network implementation for pod is
totally the same as for single container
Pod
Infra
container
Container A Container B
--net=container:pause
/proc/{pid}/ns/net -> net:[4026532483]
Kubernetes uses CNI
• CNI plugin
• e.g. Calico, Flannel etc
• The kubelet cni flags:
• --network-plugin=cni
• --network-plugin-dir=/etc/cni/net.d
• CNI is very simple
1.Kubelet creates a network namespace for Pod
2.Kubelet invokes CNI plugin to configure the NS (interface
name, IP, MAC, gateway, bridge name …)
3.Infra container in Pod join this network namespace
Tips
• host < calico(bgp) < calico(ipip) = flannel(vxlan) = docker(vxlan) < flannel(udp) < weave(udp)
• Test graph comes from: http://cmgs.me/life/docker-network-cloud
Calico Flannel Weave Docker Overlay Network
Network Model Pure Layer-3 Solution VxLAN or UDP Channel VxLAN or UDP Channel VxLAN
Calico
• Step 1: Run calico-node image as DaemonSet
Calico
• Step 2: Download and enable calico cni plugin
Calico
• Step 3: Add calico network controller
• Done!
10. Persistent volume?
Persistent Volumes
• -v host_path:container_path
1.Attach networked storage to host path
1. mounted to host_path
2.Mount host path as container volume
1. bind mount container_path with host_path
3. Independent volume control loop
Officially Supported PVs
• GCEPersistentDisk
• AWSElasticBlockStore
• AzureFile
• FC (Fibre Channel)
• NFS
• iSCSI
• RBD (Ceph Block Device)
• CephFS
• Cinder (OpenStack block storage)
• Glusterfs
• VsphereVolume
• HostPath (single node testing only)
• more than 20+
• Write your own volume plugin: FlexVolume
1. Implement 10 methods
2. Put binary/shell in plugin directory
• example: LVM as k8s volume
Production ENV Volume Model
Persistent Volumes
PersistentVolumeClaims Pod
Host
path
networked
storage
Pod Pod
mountPath mountPath
Key point: 职责分离
PV & PVC
• System Admin:
• $ kubectl create -f nfs-pv.yaml
• create a volume with access mode, capacity, recycling mode
• Dev:
• $ kubectl create -f pv-claim.yaml
• request a volume with access mode, resource, selector
• $ kubectl create -f pod.yaml
More …
• GC
• Health check
• Container lifecycle hook
• Jobs (batch)
• Pod affinity and binding
• Dynamic provisioning
• Rescheduling
• CronJob
• Logging and monitoring
• Network policy
• Federation
• Container capabilities
• Resource quotas
• Security context
• Security polices
• GPU scheduling
Summary
• Q: Where are all these control panel ideas come from?
• A: Kubernetes = “Borg” + “Container”
• Kubernetes is a set of methodology for using containers based on past
10+ yr’s exp in Google Inc.
• “不不要摸着⽯石头过河”
• Kubernetes is a container centric DevOps/Workload orchestration system
• Not a “CI/CD”, “Micro-service” focused container cloud
Growing Adopters
• Public Cloud
• AWS
• Microsoft Azure (acquired Deis)
• Google Cloud
• 腾讯云
• 百度AI
• 阿⾥里里云
Enterprise Users
THE END
@resouer
harryzhang@zju.edu.cn

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Kubernetes Walk Through from Technical View

  • 2. Kubernetes • Created by Google Borg/Omega team • Founded and operated by CNCF (Linux Foundation) • Container orchestration, scheduling and management • One of the most popular open source project in the world
  • 8. Example kubelet SyncLoop kubelet SyncLoop proxy proxy 3.1 New container detected 3.2 Bind container to a node etcd scheduler api-server
  • 9. Example kubelet SyncLoop kubelet SyncLoop proxy proxy 4.1 Detected bind operation 4.2 Start container on this machine etcd scheduler api-server
  • 10. Take Aways • Independent control loops • loosely coupled • high performance • easy to customize and extend • “Watch” object change • Decide next step based on state change • not edge driven (event), level driven (state)
  • 11. {Pod} = a group of containers
  • 12. Co-scheduling • Tow containers: • App: generate log files • LogCollector: read and redirect logs to storage • Request MEM: • App: 1G • LogCollector: 0.5G • Available MEM: • Node_A: 1.25G • Node_B: 2G • What happens if App is scheduled to Node_A first?
  • 13. Pod • Deeply coupled containers • Atomic scheduling/placement unit • Shared namespace • network, IPC etc • Shared volume • Process group in container cloud
  • 14. Why co-scheduling? • It’s about using container in right way: • Lesson learnt from Borg: “workloads tend to have tight relationship”
  • 15. Ensure Container Order • Decouple web server and application • war file container • tomcat container
  • 16. • Wrong! Multiple Apps in One Container? Master Pod kube-apiserver kube-scheduler controller-manager ⽇日志看不不到 是否running没法判断 运维操作困难 出错定位麻烦,不不知道是哪个挂了了,频繁登陆容器器
  • 17. Copy Files from One to Another? • Wrong! Master Pod kube-apiserver kube-scheduler controller-manager /etc/kubernetes/ssl
  • 18. Connect to Peer Container thru IP? • Wrong! Master Pod kube-apiserver kube-scheduler controller-manager network namespace
  • 19. So this is Pod • Design pattern in container world • decoupling • reuse & refactoring • Describe more real-world workloads by container • e.g. ML • Parameter server and trainer in same Pod
  • 21. 1. How Kubernetes schedule workloads?
  • 22. Resource Model • Compressible resources •  Hold no state •  Can be taken away very quickly •  “Merely” cause slowness when revoked •  e.g. CPU • Non-compressible resources • Hold state • Are slower to be taken away • Can fail to be revoked • e.g. Memory, disk space Kubernetes (and Docker) can only handle CPU & Memory Don’t handle things like memory bandwidth, disk time, cache, network bandwidth, ... (yet)
  • 23. Resource Model • Request: amount of a resource allowed to be used, with a strong guarantee of availability •  CPU (seconds/second), RAM (bytes) •  Scheduler will not over-commit requests • Limit: max amount of a resource that can be used, regardless of guarantees • scheduler ignores limits • Mapping to Docker • —cpu-shares=requests.cpu • —cpu-quota=limits.cpu • —cpu-period=100ms • —memory=limits.memory
  • 24. QoS Tiers and Eviction • Guaranteed • limits is set for all resources, all containers • limits == requests (if set) • Be killed until they exceed their limits • or if the system is under memory pressure and there are no lower priority containers that can be killed. • Burstable • requests is set for one or more resources, one or more containers • limits (if set) != requests • killed once they exceed their requests and no Best-Effort pods exist when system under memory pressure • Best-Effort • requests and limits are not set for all of the resources, all containers • First to get killed if the system runs out of memory
  • 25. Scheduler • Predicates • NoDiskConflict • NoVolumeZoneConflict • PodFitsResources • PodFitsHostPorts • MatchNodeSelector • MaxEBSVolumeCount • MaxGCEPDVolumeCount • CheckNodeMemoryPressure • eviction, QoS tiers • CheckNodeDiskPressure • Priorities • LeastRequestedPriority • BalancedResourceAllocation • SelectorSpreadPriority • CalculateAntiAffinityPriority • ImageLocalityPriority • NodeAffinityPriority • Design tips: • watch and sync podQueue • schedule based on cached info • optimistically bind • predicates is paralleled between nodes • priorities are paralleled between functions in Map-Reduce way
  • 26. Multi-Scheduler The 2nd scheduler • Tips: annotation: system usage labels • Do NOT abuse labels
  • 28. Deployment • Replicas with control • Bring up a Replica Set and Pods. • Check the status of a Deployment. • Update that Deployment (e.g. new image, labels). • Rollback to an earlier Deployment revision. • Pause and resume a Deployment.
  • 29. Create • ReplicaSet • Next generation of ReplicaController • —record: record command in the annotation of ‘nginx-deployment’
  • 30. Check • DESIRED: .spec.replicas • CURRENT: .status.replicas • UP-TO-DATE: contains the latest pod template • AVAILABLE: pod status is ready (running)
  • 31. Update • kubectl set image • will change container image • kubectl edit • open an editor and modify your deployment yaml • RollingUpdateStrategy • 1 max unavailable • 1 max surge • can also be percentage • Does not kill old Pods until a sufficient number of new Pods have come up • Does not create new Pods until a sufficient number of old Pods have been killed. trigger
  • 32. Update Process • The update process is coordinated by Deployment Controller • Create: Replica Set (nginx-deployment-2035384211) and scaled it up to 3 replicas directly. • Update: • created a new Replica Set (nginx-deployment-1564180365) and scaled it up to 1 • scaled down the old Replica Set to 2 • continued scaling up and down the new and the old Replica Set, with the same rolling update strategy. • Finally, 3 available replicas in the new Replica Set, and the old Replica Set is scaled down to 0.
  • 33. Rolling Back • Check reversions • Roll back to reversion
  • 34. Pausing & Resuming (Canary) • Tips • blue-green deployment: duplicated infrastructure • canary release: share same infrastructure • rollback resumed deployment is WIP • old way: kubectl rolling-update rc-1 rc-2
  • 35. 3. Deploy Daemon workload to every Node?
  • 36. DaemonSet • Spread daemon pod to every node • DaemonSet Controller • bypass default scheduler • even on unschedulable nodes • e.g. bootstrap
  • 38. Horizontal Pod Autoscaling • Tips • Scale out/in • TriggeredScaleUp (GCE, AWS, will add more) • Support for custom metrics
  • 39. Custom Metrics • Endpoint (Location to collect metrics from) • Name of metric • Type (Counter, Gauge, ...) • Data Type (int, float) • Units (kbps, seconds, count) • Polling Frequency • Regexps (Regular expressions to specify which metrics to collect and how to parse them) • The metric will be added to pod as ConfigMap volume Prometheus Nginx
  • 40. 5. Pass information to workloads?
  • 41. ConfigMap • Decouple configuration from image • configuration is a runtime attribute • Can be consumed by pods thru: • env • volumes
  • 42. ConfigMap Volume • No need to use Persistent Volume • Think about Etcd
  • 43. Secret • Tip: credentials for accessing the k8s API is automatically added to your pods as secret
  • 44. 6. Read information from system itself?
  • 45. Downward Api • Get these inside your pod as ENV or volume • The pod’s name • The pod’s namespace • The pod’s IP • A container’s cpu limit • A container’s cpu request • A container’s memory limit • A container’s memory request
  • 47. Service • The unified portal of replica Pods • Portal IP:Port • External load balancer • GCE • AWS • HAproxy • Nginx • OpenStack LB
  • 48. Service Implementation Tip: ipvs solution works in nat mode which is the same with this iptables way $ iptables-save | grep my-service -A KUBE-SERVICES -d 10.0.0.116/32 -p tcp -m comment --comment "default/my-service: cluster IP" -m tcp --dport 8001 -j KUBE-SVC-KEAUNL7HVWWSEZA6 -A KUBE-SVC-KEAUNL7HVWWSEZA6 -m comment --comment "default/my-service:" --mode random -j KUBE-SEP-6XXFWO3KTRMPKCHZ -A KUBE-SVC-KEAUNL7HVWWSEZA6 -m comment --comment "default/my-service:" --mode random -j KUBE-SEP-57KPRZ3JQVENLNBRZ -A KUBE-SEP-6XXFWO3KTRMPKCHZ -p tcp -m comment --comment "default/my-service:" -m tcp -j DNAT --to-destination 172.17.0.2:80 -A KUBE-SEP-57KPRZ3JQVENLNBRZ -p tcp -m comment --comment "default/my-service:" -m tcp -j DNAT --to-destination 172.17.0.3:80
  • 49. Publishing Services • Use Service.Type=NodePort • <node_ip>:<node_port> • External IP • IPs route to one or more cluster nodes (e.g. floating IP) • Use external LoadBalancer • Require support from IaaS (GCE, AWS, OpenStack) • Deploy a service-loadbalancer (e.g. HAproxy) • Official guide: https://github.com/kubernetes/contrib/tree/master/service-loadbalancer
  • 50. Ingress • The next generation external Service load balancer • Deployed as a Pod on dedicated Node (with external network) • Implementation • Nginx, HAproxy, GCE L7 • External access for service • SSL support for service • … s1http://foo.bar.com <IP_of_Ingress_node> http://foo.bar.com/foo
  • 53. StatefulSet: “clustered applications” • Ordinal index • startup/teardown ordering • Stable hostname • Stable storage • linked to the ordinal & hostname • Databases like MySQL or PostgreSQL • single instance attached to a persistent volume at any time • Clustered software like Zookeeper, Etcd, or Elasticsearch, Cassandra • stable membership. Update StatefulSet: Scale: create/delete one by one Scale in: will not delete old persistent volume
  • 54. StatefulSet StatefulSet Example cassandra-0 cassandra-1 volume 0 volume 1 cassandra-0.cassandra.default.svc.cluster.local cassandra-1.cassandra.default.svc.cluster.local $ kubectl patch petset cassandra -p '{"spec":{"replicas":10}}'
  • 56. One Pod One IP • Network sharing is important for affiliate containers • Not all containers need independent network • Network implementation for pod is totally the same as for single container Pod Infra container Container A Container B --net=container:pause /proc/{pid}/ns/net -> net:[4026532483]
  • 57. Kubernetes uses CNI • CNI plugin • e.g. Calico, Flannel etc • The kubelet cni flags: • --network-plugin=cni • --network-plugin-dir=/etc/cni/net.d • CNI is very simple 1.Kubelet creates a network namespace for Pod 2.Kubelet invokes CNI plugin to configure the NS (interface name, IP, MAC, gateway, bridge name …) 3.Infra container in Pod join this network namespace
  • 58. Tips • host < calico(bgp) < calico(ipip) = flannel(vxlan) = docker(vxlan) < flannel(udp) < weave(udp) • Test graph comes from: http://cmgs.me/life/docker-network-cloud Calico Flannel Weave Docker Overlay Network Network Model Pure Layer-3 Solution VxLAN or UDP Channel VxLAN or UDP Channel VxLAN
  • 59. Calico • Step 1: Run calico-node image as DaemonSet
  • 60. Calico • Step 2: Download and enable calico cni plugin
  • 61. Calico • Step 3: Add calico network controller • Done!
  • 63. Persistent Volumes • -v host_path:container_path 1.Attach networked storage to host path 1. mounted to host_path 2.Mount host path as container volume 1. bind mount container_path with host_path 3. Independent volume control loop
  • 64. Officially Supported PVs • GCEPersistentDisk • AWSElasticBlockStore • AzureFile • FC (Fibre Channel) • NFS • iSCSI • RBD (Ceph Block Device) • CephFS • Cinder (OpenStack block storage) • Glusterfs • VsphereVolume • HostPath (single node testing only) • more than 20+ • Write your own volume plugin: FlexVolume 1. Implement 10 methods 2. Put binary/shell in plugin directory • example: LVM as k8s volume
  • 65. Production ENV Volume Model Persistent Volumes PersistentVolumeClaims Pod Host path networked storage Pod Pod mountPath mountPath Key point: 职责分离
  • 66. PV & PVC • System Admin: • $ kubectl create -f nfs-pv.yaml • create a volume with access mode, capacity, recycling mode • Dev: • $ kubectl create -f pv-claim.yaml • request a volume with access mode, resource, selector • $ kubectl create -f pod.yaml
  • 67. More … • GC • Health check • Container lifecycle hook • Jobs (batch) • Pod affinity and binding • Dynamic provisioning • Rescheduling • CronJob • Logging and monitoring • Network policy • Federation • Container capabilities • Resource quotas • Security context • Security polices • GPU scheduling
  • 68. Summary • Q: Where are all these control panel ideas come from? • A: Kubernetes = “Borg” + “Container” • Kubernetes is a set of methodology for using containers based on past 10+ yr’s exp in Google Inc. • “不不要摸着⽯石头过河” • Kubernetes is a container centric DevOps/Workload orchestration system • Not a “CI/CD”, “Micro-service” focused container cloud
  • 69. Growing Adopters • Public Cloud • AWS • Microsoft Azure (acquired Deis) • Google Cloud • 腾讯云 • 百度AI • 阿⾥里里云 Enterprise Users