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MicroSuggest: Kernel-Aware Microservice Decomposition
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AI
DOI：10.1007/978-3-032-02215-8_24.png)
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Abstract
En 中文
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.
Keywords:
Microservice decomposition
System call analysis
Collision score
Function interaction graph
Kernel-aware design
Journal
B
IF:
0
Papers:
26
Citations:
0