This repository includes codes and data for CausalRCA. CausalRCA is a root cause localization framework, including monitoring metrics collection, causal structure learning and root cause inference.

We deploy the sock-shop with Kubernetes on several VMs in the cloud and inject anomalies to simulate performance issues of a running microservice application.

We collect data with data_collection_all_services.ipynb and put all data in the folder data_collected.

We collect both service-level and resource-level data. At the service level, we collect the latency of each service. At the resource level, we collect container resource-related metrics, including CPU usage, memory usage, disk read and write, and network receive and transmit bytes.

To run these python files, first install requirements with

pip install -r requirements.txt

We provide codes for benchmark methods and CausalRCA, including the three experiments in our paper for latency, single-service, and full-service tests.

For benchmark test, check bench_test-latency.ipynb, bench_test-single_service.ipynb, or bench_test-all_service.ipynb.

For CausalRCA, run train_latency.py, train_single_service.py, or train_all_services.py.

To repeat experiments, test_latency_per.sh, test_single_service_per.sh, and test_all_service_per.sh can be used.