Note: This repository is a fork of the original repository, but with small modifications regarding the evaluation code, README, etc.
This repository contains a self-adaptive autoscaler for DSP systems called Daedalus. Daedalus monitors a running DSP job and builds performance models to estimate the maximum processing capacity at different scale-outs. By predicting recovery times and anticipating the future workload with time series forecasting, Daedalus enables long-lived scaling actions that can process the incoming workload, achieve reasonable latencies, and minimize resource usage.
The dsp_jobs directory contains three representative jobs for Apache Flink, WordCount, the Yahoo Streaming Benchmark, and a Traffic Monitoring job. It also contains WordCount for Kafka Streams. Jars for the jobs can be built using mvn clean package. With the build_images.sh script, the jobs are uploaded to images in order to be deployed in a Kubernetes cluster.
The helm_charts directory contains a collection of technologies required to run a DSP job. Desired technologies can be enabled in the main values.yaml file.
Install the enabled helm charts using:
helm install -n [namespace] [deployment_name] [path_to_helm_charts]
Uninstall with:
helm uninstall -n [namespace] [deployment_name]
Before running Daedalus, adjust the configurations as desired in config.py. After installing the required dependencies, it can be run using:
python main.py