# CausalRCA_code

**Repository Path**: cherrute/CausalRCA_code

## Basic Information

- **Project Name**: CausalRCA_code
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-12-31
- **Last Updated**: 2025-12-31

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# CausalRCA_code

## Description

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.  

![image](https://github.com/AXinx/CausalRCA_code/blob/master/figures/diagnosis-fram.png)

## Data 

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.


## Code 

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.