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 | ||
| 11:5515m Paper | 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 | ||
| 12:1015m Paper | 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 | ||
| 12:2515m Paper | 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 | ||
| 12:4015m Paper | 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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