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FaaS

Background

  • Function As Services is a recently popular cloud computing solution. It provides a platform for users to develop, run and manage application functionalities without the complexity of building and maintaining the infrastructure.[1]
  • Easegress provides a business controller for implementing these zero-infrastructure-maintaining and auto-scaling requirements.

Easegress works with FaaS

  • Isolation: separate Control logic and Business logic
  • Traffic originated: Original near traffic, easier to integrate
  • Resource saving: reusing Easegress+K8s machine resources.
  • Pay what you used: reducing small customize features' developing and maintenance cost.

Examples

Scenario 1: Run a FaaS function beside Easegress

  • After implementing your business logic
  1. Create a FaaSController[2]
echo 'name: faascontroller
kind: FaaSController
provider: knative             # FaaS provider kind, currently we only support Knative
syncInterval: 10s

httpServer:
    http3: false
    port: 10083
    keepAlive: true
    keepAliveTimeout: 60s
    maxConnections: 10240

knative:
   networkLayerURL: http:https://${knative_kourier_clusterIP}
   hostSuffix: example.com '| egctl object create
  1. Deploy a function into Easegress and Knative, prepare a YAML content as below:
name:           "demo"
image:          "${image_url}"
port:           8089
autoScaleType:  "concurrency"
autoScaleValue: "100"
  • Save it into /home/easegress/function.yaml, using command to deploy it in Easegress: Note this command should be run in Easegress' instance environment and ${eg_apiport} should be replaced with the real working port, ${image_url} should be replaced with pullable image URL.
$ curl --data-binary @/home/easegress/function.yaml -X POST -H 'Content-Type: text/vnd.yaml' http:https://127.0.0.1:${eg_apiport}/apis/v1/faas/faascontroller
  1. Get the function's status, make sure it is in active status
$ curl http:https://127.0.0.1:${eg_apiport}/apis/v1/faas/faascontroller/demo
spec:
  name: demo
  image: dev.local/demo:1.0
  port: 8089
  autoScaleType: rps
  autoScaleValue: "1000"
  minReplica: 0
  maxReplica: 0
  limitCPU:
  limitMemory:
  requestCPU:
  requestMemory:
  requestAdaptor:
    host: ""
    method: ""
    header:
      del: {}
      set: {}
      add: {}
    body: ""
status:
  name: demo10
  state: active
  event: ready
  extData: {}
fsm: null
  1. Request the FaaS function by Easegress HTTP traffic gate with X-FaaS-Func-Name: demo in the HTTP header. Note: this example HTTP backend's API works on /tomcat/job/api path and its business logic is echoing back your request body and with V3 Body is content.
$ curl http:https://127.0.0.1:10083/tomcat/job/api -H "X-FaaS-Func-Name: demo" -X POST -d ‘{"megaease":"Hello Easegress+Knative"}’
V3 Body is
‘{megaease:Hello Easegress+Knative}’%

Scenario 2: Limit FaaS function resources using

  • You want to make sure at the maximum instance number can only be under 50, and it can only "180m" CPU and "100Mi" memory usage maximum allowed per instance. To providing meaningful resources amount for the function, you also want to make sure one instance has at least a "100m" CPU and "50mi" memory provision. (The CPU and memory limitation usage value comes from Kubernetes resource).
name: demo
#...

limitedMemory: "200Mi"
limitedCPU: "180m"
requireMemory: "100Mi"
requireCPU: "100m"
minReplica: 0
maxReplica: 50

  • For the full YAML, see here

  • Add the configuration above in #Scenario 1's /home/easegress/function.yaml

  1. Stop the function execution by using command
$ curl http:https://127.0.0.1:${eg_apiport}/apis/v1/faas/faascontroller/demo/stop -X PUT
  • The function will become inactive then we can update the resource limitation safely.
  1. Update the function's spec
$ curl --data-binary @/home/easegress/function.yaml -X PUT -H 'Content-Type: text/vnd.yaml' http:https://127.0.0.1:${eg_apiport}/apis/v1/faas/faascontroller/demo
  1. Verify the update
  • Waiting for the function starts successfully and becomes active
  • Request the function with step4 in Scenario 1.

Scenario 3: Longlife FaaS function

  • In the same special cases, you may want your FaaS function to have at least one instance running beside Easegress.
name: demo
#...
minReplica:  1
#...
  • For the full YAML, see here

  1. Modifying the minReplica above in #Scenario 1's /home/easegress/function.yaml

  2. Update the function spec and verify it as in Scenario 2's steps 2 - 3.

Scenario 4: Autoscaling FaaS Function according to rps

  • If you don't need to control the function's allowed request precisely, RPS based autoscaling is a good choice.
name: demo
#...
autoScaleType:  "rps"
autoScaleValue: "6000"
#...
  • For the full YAML, see here

  1. Modifying the autoScaleType and autoScaleValue" above in #Scenario 1's /home/easegress/function.yaml`

  2. Update the function spec and verify it as in Scenario 2's step 2 - 3.

References

  1. https://en.wikipedia.org/wiki/Function_as_a_service
  2. https://github.com/megaease/easegress/blob/main/doc/faascontroller.md

Resource limiter

name:           demo
image:          "${image_url}"
port:           8089
autoScaleType:  "concurrency"
autoScaleValue: "100
limitedMemory: "200Mi"
limitedCPU:    "180m"
requireMemory: "100Mi"
requireCPU:    "100m"
minReplica:    0
maxReplica:    50

Long life function

name:           demo
image:          "${image_url}"
port:           8089
autoScaleType:  "concurrency"
autoScaleValue: "100"
limitedMemory: "200Mi"
limitedCPU:    "180m"
requireMemory: "100Mi"
requireCPU:    "100m"
minReplica:    1 
maxReplica:    50

RPS autoscaling

name:           demo
image:          "${image_url}"
port:           8089
autoScaleType:  "rps"
autoScaleValue: "6000"
limitedMemory: "200Mi"
limitedCPU:    "180m"
requireMemory: "100Mi"
requireCPU:    "100m"
minReplica:    0 
maxReplica:    50