Distributed online extraction of a fluid model for microservice applications using local tracing data
Ruuskanen, Johan LU
and Cervin, Anton
LU
(2022)
2022 IEEE 15th International Conference on Cloud Computing (CLOUD)
- Abstract
- Dynamic resource management is a difficult problem in modern microservice applications. Many proposed methods rely on the availability of an analytical performance model, often based on queueing theory. Such models can always be hand-crafted, but this takes time and requires expert knowledge. Various methods have been proposed that can automatically extract models from logs or tracing data. However, they are often intricate, requiring off-line stages and advanced algorithms for retrieving the service-time distributions. Furthermore, the resulting models can be complex and unsuitable for online evaluation. Aiming for simplicity, we in this paper introduce a general queuing network model for microservice applications that can be (i) quickly... (More)
- Dynamic resource management is a difficult problem in modern microservice applications. Many proposed methods rely on the availability of an analytical performance model, often based on queueing theory. Such models can always be hand-crafted, but this takes time and requires expert knowledge. Various methods have been proposed that can automatically extract models from logs or tracing data. However, they are often intricate, requiring off-line stages and advanced algorithms for retrieving the service-time distributions. Furthermore, the resulting models can be complex and unsuitable for online evaluation. Aiming for simplicity, we in this paper introduce a general queuing network model for microservice applications that can be (i) quickly and accurately solved using a refined mean-field fluid model and (ii) completely extracted at runtime in a distributed fashion from common local tracing data at each service. The fit of the model and the prediction accuracies under system perturbations are evaluated in a cloud-based microservice application and are found to be accurate. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/18030198-c398-4d0b-a09e-6b58290cfb10
- author
- Ruuskanen, Johan
LU
and Cervin, Anton
LU
- organization
- publishing date
- 2022-07
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- 2022 IEEE 15th International Conference on Cloud Computing (CLOUD)
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- 2022 IEEE 15th International Conference on Cloud Computing (CLOUD)
- conference location
- Barcelona, Spain
- conference dates
- 2022-07-11 - 2022-07-15
- external identifiers
-
- scopus:85137610665
- DOI
- 10.1109/CLOUD55607.2022.00037
- project
- Event-Based Information Fusion for the Self-Adaptive Cloud
- language
- English
- LU publication?
- yes
- id
- 18030198-c398-4d0b-a09e-6b58290cfb10
- date added to LUP
- 2022-08-26 08:23:41
- date last changed
- 2022-11-30 09:08:00
@inproceedings{18030198-c398-4d0b-a09e-6b58290cfb10,
abstract = {{Dynamic resource management is a difficult problem in modern microservice applications. Many proposed methods rely on the availability of an analytical performance model, often based on queueing theory. Such models can always be hand-crafted, but this takes time and requires expert knowledge. Various methods have been proposed that can automatically extract models from logs or tracing data. However, they are often intricate, requiring off-line stages and advanced algorithms for retrieving the service-time distributions. Furthermore, the resulting models can be complex and unsuitable for online evaluation. Aiming for simplicity, we in this paper introduce a general queuing network model for microservice applications that can be (i) quickly and accurately solved using a refined mean-field fluid model and (ii) completely extracted at runtime in a distributed fashion from common local tracing data at each service. The fit of the model and the prediction accuracies under system perturbations are evaluated in a cloud-based microservice application and are found to be accurate.}},
author = {{Ruuskanen, Johan and Cervin, Anton}},
booktitle = {{2022 IEEE 15th International Conference on Cloud Computing (CLOUD)}},
language = {{eng}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
title = {{Distributed online extraction of a fluid model for microservice applications using local tracing data}},
url = {{http://dx.doi.org/10.1109/CLOUD55607.2022.00037}},
doi = {{10.1109/CLOUD55607.2022.00037}},
year = {{2022}},
}