Kmon: An In-kernel Transparent Monitoring System for Microservice Systems with eBPF
Currently, the architecture of software systems is shifting from “monolith” to “microservice” which is an important enabling technology of cloud native systems. Since the advantages of microservice in agility, efficiency, and scaling, it has become the most popular architecture in the industry. However, as the increase of microservice complexity and scale, it becomes challenging to monitor such a large number of microservices. Traditional monitoring techniques such as end-to-end tracing cannot well fit microservice environment, because they need code instrumentation with great effort. Moreover, they cannot explore the fine-grained internal states of microservice instances. To tackle this problem, we propose Kmon, which is an In kernel transparent monitoring system for microservice systems with extended Berkeley Packet Filter (eBPF). Kmon can provide multiple kinds of run-time information of micrservices such as latency, topology, performance metrics with a low overhead.
Sat 29 MayDisplayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change
11:55 - 12:55 | Technical paper session #1CloudIntelligence 2021 at CloudIntelligence Room Chair(s): Qingwei Lin Microsoft Research, Beijing, China | ||
11:55 15mPaper | PerfEstimator: A Generic and Extensible Performance Estimator for Data Parallel DNN Training CloudIntelligence 2021 Chengru Yang University of Science and Technology of China, Zhehao Li University of Science and Technology of China, Chaoyi Ruan University of Science and Technology of China, Guanbin Xu University of Science and Technology of China, Cheng Li University of Science and Technology of China, Ruichuan Chen Nokia Bell Labs, Feng Yan University of Nevada Reno | ||
12:10 15mPaper | Kmon: An In-kernel Transparent Monitoring System for Microservice Systems with eBPF CloudIntelligence 2021 Tianjun Weng Sun Yat-Sen University, Wanqi Yang Sun Yat-Sen University, Guangba Yu Sun Yat-Sen University, Pengfei Chen Sun Yat-Sen University, Jieqi Cui Sun Yat-Sen University, Chuanfu Zhang Sun Yat-Sen University | ||
12:25 15mPaper | TraceLingo: Trace representation and learning for performance issue diagnosis in cloud services CloudIntelligence 2021 Yong Xu Microsoft, China, Yaokang Zhu Microsoft Research Asia, Bo Qiao Microsoft Research, Beijing, China, Hongshu Che Microsoft Research, Beijing, China, Pu Zhao Microsoft Research, Beijing, China, Xu Zhang Microsoft Research, Beijing, China, Ze Li Microsoft, USA, Yingnong Dang Microsoft, USA, Qingwei Lin Microsoft Research, Beijing, China | ||
12:40 15mPaper | MicroDiag: Fine-grained Performance Diagnosis for Microservice Systems CloudIntelligence 2021 Li Wu Elastisys AB/Technische Universität Berlin, Johan Tordsson Elastisys AB, Jasmin Bogatinovski , Erik Elmroth Elastisys AB/Umea University, Odej Kao Technische Universität Berlin |
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