Deepstitch: Deep Learning for Cross-Layer Stitching in Microservices

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

abstract

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.