Quota-based Knative Hybrid Autoscaling Operator

as in https://doi.org/10.1016/j.future.2024.06.019

Prerequisites

• go version v1.20.0+
• docker version 17.03+.
• kubectl version v1.11.3+.
• Access to a Kubernetes v1.11.3+ cluster.

Getting Started

You’ll need a Knative cluster to run against. Note: This operator was built and tested on: (Recommended for testing)

• Ubuntu version 18.04.5/6
• Kubernetes v1.23.5
• Istio
• Knative v1.8 (Serving and Eventing v1.8.5, Knative Istio Controller v1.8.0) For installing the above environment, please refer to knative_install.md file.

How it works

This project aims to follow the Kubernetes Operator pattern.

It uses Controllers, which provide a reconcile function responsible for synchronizing resources until the desired state is reached on the cluster.

For Testing

0. Kubectl apply the 3 target services as given in prequisites_service_deployment
1. Delete any DRLScalingAction CR in cluster if running the test again from the beginning
2. Run Locust Traffic Profile as given in prequisites_traffic_generator_files (Not generate traffic yet)
3. Build and Install the CRDs into the cluster (Only first time)

make                              #make all
make manifests                    #Generate CRD
kubectl apply -f config/crd/bases #Apply CRD

5. Run Quota-Based Knative HybridScaling controller (this will run in the foreground, so switch to a new terminal if you want to leave it running):

make run

6. Start Generating Traffic to Service URL using Locust UI (HostURL:8089) Note: Traffic need to be predicted before the actual new traffic change happening

• For example: Predict Traffic at every second 55 in a minute ("schedule.every().minute.at(":55").do(lambda: predict(api)")
• Then Start Generating Traffic at second >= 00 (example predicting at 0:55, new traffic change at 1:00)

7. Start DRL Agent Service (refer to https://github.com/mipearlska/DRL_knative_scale_limit_resources for installation and running guide)

• This service might take up until 1 minute to start up (loading libraries, etc.)
• Recommended Running flow: Start Prediction service (running python3 main.py) at 0:40, Start Traffic Generation at 1:00
• First Prediction will happen at 1:55, at 0:55 the Prediction service is still starting up
• If your system startup the Prediction service fast, delay the Start Prediction service time to a later second (<55)
• Reason: Prediction Service up before 0:55, first prediction happen at 0:55 while the traffic has not been generated yet (scheduled to start at 1:00)

License

Copyright 2023 mipearlska.

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.