MicroSuggest: Kernel-Aware Microservice Decomposition
Abstract
Microservice decomposition typically emphasizes logical or domain-driven boundaries, often overlooking performance bottlenecks from low-level system interactions. We present a system call-aware decomposition method that identifies and separates functions likely to interfere at the kernel level. By defining a collision score based on system call frequency and type, and using a fine-tuned Large Language Model to statically predict syscall behavior, we construct a function interaction graph for clustering. Evaluation on Python-based monoliths shows up to 30% latency reduction and improved scalability compared to traditional approaches, demonstrating the value of kernel-informed microservice design.
Contributors
Bibtex
@inproceedings{borse2025microsuggest,
title={MicroSuggest: Kernel-Aware Microservice Decomposition},
author={Borse, Harsh and Satpathy, Utkalika and Mondal, Mainack and Mitra, Bivas},
booktitle={International Conference on Big Data Analytics and Knowledge Discovery},
pages={302--308},
year={2025},
organization={Springer}
}
Cite
Borse, H., Satpathy, U., Mondal, M., & Mitra, B. (2025, August). MicroSuggest: Kernel-Aware Microservice Decomposition. In International Conference on Big Data Analytics and Knowledge Discovery (pp. 302-308). Cham: Springer Nature Switzerland.