Performance modeling of distributed data processing in microservice applications
File(s)
Published version
Author(s)
Gao, Yicheng
Casale, Giuliano
Singhal, Rekha
Type
Journal Article
Abstract
Microservice applications are increasingly adopted in distributed data processing systems, such as in mobile edge computing and data mesh architectures. However, existing performance models of such systems fall short in providing comprehensive insights into the intricate interplay between data placement and data processing. To address these issues, this article proposes a novel class of performance models that enables joint analysis of data storage access workflows, caching, and queueing contention. Our proposed models introduce a notion of access path for data items to model hierarchical data locality constraints. We develop analytical solutions to efficiently approximate the performance metrics of these models under different data caching policies, finding in particular conditions under which the underlying Markov chain admits a product-form solution. Extensive trace-driven simulations based on real-world datasets indicate that service and data placement policies based on our proposed models can respectively improve by up to 35% and 37% the average response time in edge and data mesh case studies.
Date Issued
2024-12-01
Date Acceptance
2024-07-23
Citation
ACM Transactions on Modeling and Performance Evaluation of Computing Systems, 2024, 9 (4)
ISSN
2376-3639
Publisher
Association for Computing Machinery (ACM)
Journal / Book Title
ACM Transactions on Modeling and Performance Evaluation of Computing Systems
Volume
9
Issue
4
Copyright Statement
© 2024 Copyright held by the owner/author(s). This work is licensed under a Creative Commons Attribution International 4.0 License.
License URL
Subjects
ARCHITECTURE
Computer Science
Computer Science, Information Systems
data caching
data placement
layered network
Microservice
NETWORK
performance modeling
Science & Technology
Technology
Publication Status
Published
Article Number
14
Date Publish Online
2024-10-11