research-article

Characterizing Microservice Dependency and Performance: Alibaba Trace Analysis

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Chengzhong XuAuthors Info & Claims

SoCC '21: Proceedings of the ACM Symposium on Cloud Computing

Pages 412 - 426

https://doi.org/10.1145/3472883.3487003

Published: 01 November 2021 Publication History

Abstract

Loosely-coupled and light-weight microservices running in containers are replacing monolithic applications gradually. Understanding the characteristics of microservices is critical to make good use of microservice architectures. However, there is no comprehensive study about microservice and its related systems in production environments so far. In this paper, we present a solid analysis of large-scale deployments of microservices at Alibaba clusters. Our study focuses on the characterization of microservice dependency as well as its runtime performance. We conduct an in-depth anatomy of microservice call graphs to quantify the difference between them and traditional DAGs of data-parallel jobs. In particular, we observe that microservice call graphs are heavy-tail distributed and their topology is similar to a tree and moreover, many microservices are hot-spots. We reveal three types of meaningful call dependency that can be utilized to optimize microservice designs. Our investigation on microservice runtime performance indicates most microservices are much more sensitive to CPU interference than memory interference. To synthesize more representative microservice traces, we build a mathematical model to simulate call graphs. Experimental results demonstrate our model can well preserve those graph properties observed from Alibaba traces.

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cover image ACM Conferences

SoCC '21: Proceedings of the ACM Symposium on Cloud Computing

November 2021

685 pages

ISBN:9781450386388

DOI:10.1145/3472883

Copyright © 2021 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 01 November 2021

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November 1 - 4, 2021

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https://doi.org/10.3390/sym16111470
Zha JShan XLu JZhu JLiu Z(2024)Leveraging Large Language Models for Efficient Alert Aggregation in AIOPsElectronics10.3390/electronics1322442513:22(4425)Online publication date: 12-Nov-2024
https://doi.org/10.3390/electronics13224425
Xu HCheng GYang XLiu WZhou DGuo W(2024)Multi-Dimensional Moving Target Defense Method Based on Adaptive Simulated Annealing Genetic AlgorithmElectronics10.3390/electronics1303048713:3(487)Online publication date: 24-Jan-2024
https://doi.org/10.3390/electronics13030487
Dakić P(2024)Software compliance in various industries using CI/CD, dynamic microservices, and containersOpen Computer Science10.1515/comp-2024-001314:1Online publication date: 12-Jul-2024
https://doi.org/10.1515/comp-2024-0013
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