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CausIL is an approach to estimate the causal graph for a cloud microservice system, where the nodes are the service-specific metrics while edges indicate causal dependency among the metrics. The approach considers metric variations for all the instances deployed in the system to build the causal graph and can account for auto-scaling decisions.

License: MIT License

Python 93.19% Jupyter Notebook 6.81%
causal-graphs instances microservice reliability

causil's Introduction

Hi, I am Sarthak 👋

I am Sarthak Chakraborty, pursuing PhD in Computer Science at University of Illinois Urbana-Champaign (UIUC). Prior to this, I was working as a Research Associate 2 at the Adobe Research, Bangalore. I was in the Systems and Insights group. I graduated from IIT Kharagpur holding a Dual Degree (B.Tech + M.Tech Integrated) from the Department of Computer Science and Engineering. I am interested in the fields of ML for Systems, Distributed Systems, System Design, Deep Learning and Operating Systems.

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