Distributed online extraction of a fluid model for microservice applications using local tracing data

Research output: Chapter in Book/Report/Conference proceedingPaper in conference proceedingpeer-review

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.
Original languageEnglish
Title of host publication 2022 IEEE 15th International Conference on Cloud Computing (CLOUD)
PublisherIEEE - Institute of Electrical and Electronics Engineers Inc.
DOIs
Publication statusPublished - 2022 Jul
Event 2022 IEEE 15th International Conference on Cloud Computing (CLOUD) - Barcelona, Spain, Barcelona, Spain
Duration: 2022 Jul 112022 Jul 15
Conference number: 15
https://conferences.computer.org/cloud/2022/

Conference

Conference 2022 IEEE 15th International Conference on Cloud Computing (CLOUD)
Country/TerritorySpain
CityBarcelona
Period2022/07/112022/07/15
Internet address

Subject classification (UKÄ)

  • Control Engineering

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