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A Proactive Cloud Application Auto-Scaler using Reinforcement Learning

Heimerson, Albin LU orcid ; Eker, Johan LU orcid and Årzén, Karl-Erik LU orcid (2022) 15th ACM-IEEE International Conference on Formal Methods and Models for System Design
Abstract
This work explores the use of reinforcement learning to design a proactive cloud resource auto-scaler that is able to predict usage across a distributed microservice application. The focus is on serving time-sensitive workloads, e.g., industrial automation, connected XR/VR (eXtended Reality/Virtual Reality), etc., where each job has a deadline and there is some cost associated with missing a deadline. A simple workload model, as well as a microservice application model, is presented. A reinforcement learning agent is trained to identify workloads and predict needed utilization for identified service chains. The results are compared to standard purely reactive techniques.
Please use this url to cite or link to this publication:
author
; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
epub
subject
host publication
UCC '22: Proceedings of the 15th IEEE/ACM International Conference on Utility and Cloud Computing
conference name
15th ACM-IEEE International Conference on Formal Methods and Models for System Design
conference location
New York, United States
conference dates
2017-09-29 - 2017-10-02
external identifiers
  • scopus:85150676569
ISBN
9781665460873
DOI
10.1109/UCC56403.2022.00040
project
AutoDC
Event-Based Control of Stochastic Systems with Application to Server Systems
language
English
LU publication?
yes
id
980385f4-ff4b-413b-af01-554c56552369
date added to LUP
2023-01-23 14:27:02
date last changed
2023-11-22 16:43:04
@inproceedings{980385f4-ff4b-413b-af01-554c56552369,
  abstract     = {{This work explores the use of reinforcement learning to design a proactive cloud resource auto-scaler that is able to predict usage across a distributed microservice application. The focus is on serving time-sensitive workloads, e.g., industrial automation, connected XR/VR (eXtended Reality/Virtual Reality), etc., where each job has a deadline and there is some cost associated with missing a deadline. A simple workload model, as well as a microservice application model, is presented. A reinforcement learning agent is trained to identify workloads and predict needed utilization for identified service chains. The results are compared to standard purely reactive techniques.}},
  author       = {{Heimerson, Albin and Eker, Johan and Årzén, Karl-Erik}},
  booktitle    = {{UCC '22: Proceedings of the 15th IEEE/ACM International Conference on Utility and Cloud Computing}},
  isbn         = {{9781665460873}},
  language     = {{eng}},
  month        = {{12}},
  title        = {{A Proactive Cloud Application Auto-Scaler using Reinforcement Learning}},
  url          = {{https://lup.lub.lu.se/search/files/141037039/DML_ICC_2022_ServiceMeshRL_short_No_template_.pdf}},
  doi          = {{10.1109/UCC56403.2022.00040}},
  year         = {{2022}},
}