Flux Research Group / School of Computing

Deepstitch: Deep Learning for Cross-Layer Stitching in Microservices

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Cai (Richard) Li, Min Du, Hyunseok Chang, Sarit Mukherjee, and Eric Eide

Proceedings of the 6th International Workshop on Container Technologies and Container Clouds (WoC) 2020.

DOI: 10.1145/3429885.3429965



While distributed application-layer tracing is widely used for performance diagnosis in microservices, its coarse granularity at the service level limits its applicability towards detecting more fine-grained system level issues. To address this problem, cross-layer stitching of tracing information has been proposed. However, all existing cross-layer stitching approaches either require modification of the kernel or need updates in the application-layer tracing library to propagate stitching information, both of which add further complex modifications to existing tracing tools. This paper introduces Deepstitch, a deep learning based approach to stitch cross-layer tracing information without requiring any changes to existing application layer tracing tools. Deepstitch leverages a global view of a distributed application composed of multiple services and learns the global system call sequences across all services involved. This knowledge is then used to stitch system call sequences with service-level traces obtained from a deployed application. Our proof of concept experiments show that the proposed approach successfully maps application-level interaction into the system call sequences and can identify thread-level interactions.