RCAEval 0.0.7

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🕵️ Root Cause Analysis for Microservices based on Causal Inference: How Far Are We?

Download Experimental Data & Supplementary Material

You can download our supplementary material and all the data we used for this research from this Google Drive.

Installation

We maintain 3 environments for different methods that could be installed easily as follows.

OS & Hardware requirements

• Ubuntu 22.04
• 8 CPU, 16GB RAM, 30GB free disk.

Pre-installation

Open your terminal and run commands

sudo apt update -y
sudo apt install -y build-essential \
  libxml2 libxml2-dev zlib1g-dev \
  python3-tk graphviz

sudo add-apt-repository ppa:deadsnakes/ppa
sudo apt update -y
sudo apt-get install -y python3.8 python3.8-dev python3.8-venv
sudo apt-get install -y python3.10 python3.10-dev python3.10-venv

Install the Dev environment

By installing this environment, you can run the following:

• PC-based, FCI-based, LiNGAM-based, GES-based
• CausalRCA
• CIRCA, Nsigma, Dummy

python3.10 -m venv env-dev
. env-dev/bin/activate
pip install pip==20.0.2
pip install -e .[dev]

Install the RCD environment

By installing this environment, you can run the RCD algorithm

python3.8 -m venv env-rcd
. env-rcd/bin/activate
pip install pip==20.0.2
pip install -e .[rcd]
bash script/link.sh

Install the fGES environment

By installing this environment, you can run the fGES-based algorithm

python3.8 -m venv env-fges
. env-fges/bin/activate

pip install pip==20.0.2
pip install -e .[rcd]

cd LIB
pip install -e .
cd ..

# it MUST be performed in this order
sudo apt-get install -y gcc graphviz libgraphviz-dev pkg-config
pip install dill pygobnilp
pip install -U numba
pip install category_encoders sortedcontainers fcit
pip install pgmpy
pip install feature_engine

Reproducibility

Reproduce RQ1 - Causal Discovery Performance

To reproduce the causal discovery performance, as presented in Table 3. You can download the corresponding dataset and extracted to folder ./data. Then, you can run the file graph_eval.py to obtain the results for one iteration. For example:

As presented in Table 3, PC achieves F1, F1-S, and SHD of 0.49, 0.65, and 16 on the CIRCA 10 dataset. To reproduce this results as presented in the Table 3. You can run the following commands:

python graph_eval.py -i data/syn_circa/10 -m pc -w 5

The expected output should be exactly as presented in the paper (it takes around 1 minute to run the code)

F1:   0.49
F1-S: 0.65
SHD:  16

We can replace the method pc and dataset syn_circa/10 to replicate corresponding results.

Reproduce RQ2 - Root Cause Analysis Performance

To reproduce the root cause analysis performance, as presented in Table 5. You can download the corresponding dataset and extracted to folder ./data. Then, you can run the file eval.py to reproduce the results. For example:

As presented in Table 5, NSigma [ $t_\Delta = 0$ ] achieves Avg@5 of 0.94, 1, 0.9, 0.98, and 0.67 for CPU, MEM, DISK, DELAY, and LOSS fault types on the Online Boutique dataset. To reproduce the RCA performance of NSigma [ $t_\Delta = 0$ ] as presented in the Table 5. You can run the following commands:

python eval.py -i data/online-boutique -o output-tmp -m nsigma --iter-num 10 -w 10 --length 10

Expected output after running the above code (it takes around 1 minute)

The results are exactly as presented in the paper (Table 5).

Evaluation results
s_cpu: 0.94
s_mem: 1.0
s_disk: 0.9
s_delay: 0.98
s_loss: 0.67

As presented in Table 5, NSigma [ $t_\Delta = 60$ ] achieves Avg@5 of 0.16, 0.24, 0.43, 0.55, and 0.38 for CPU, MEM, DISK, DELAY, and LOSS fault types on the Online Boutique dataset. To reproduce the RCA performance of NSigma [ $t_\Delta = 60$ ] as presented in the Table 5. You can run the following commands:

python eval.py -i data/online-boutique -o output-tmp -m nsigma --iter-num 10 -w 10 --length 10 --ad-delay 60

Expected output after running the above code (it takes around 1 minute)

The results are exactly as presented in the paper (Table 5).

Evaluation results
s_cpu: 0.16
s_mem: 0.24
s_disk: 0.43
s_delay: 0.55
s_loss: 0.38

We can replace the method nsigma by baro, pc_pagerank, fci_pagerank, rcd, e_diagnosis, etc. to replicate corresponding results.

Reproduce RQ3 - Efficiency

The efficiency is captured in our evaluation script and saved in the corresponding output directory.

Reproduce RQ4 - Data lengths

Our RQ4 relies on the scripts of RQ1 and RQ2, which we presented above.