A Proactive Cloud Application Auto-Scaler using Reinforcement Learning

Albin Heimerson; Johan Eker; Karl-Erik Årzén

doi:10.1109/UCC56403.2022.00040

A Proactive Cloud Application Auto-Scaler using Reinforcement Learning

Albin Heimerson, Johan Eker, Karl-Erik Årzén

Forskningsoutput: Kapitel i bok/rapport/Conference proceeding › Konferenspaper i proceeding › Peer review

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Sammanfattning

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.

Originalspråk	engelska
Titel på värdpublikation	UCC '22: Proceedings of the 15th IEEE/ACM International Conference on Utility and Cloud Computing
Sidor	213-220
Antal sidor	8
ISBN (elektroniskt)	9781665460873
DOI	https://doi.org/10.1109/UCC56403.2022.00040
Status	Published - 2022
Evenemang	15th ACM-IEEE International Conference on Formal Methods and Models for System Design - New York, USA Varaktighet: 2017 sep. 29 → 2017 okt. 2 Konferensnummer: 15th

Konferens

Konferens	15th ACM-IEEE International Conference on Formal Methods and Models for System Design
Land/Territorium	USA
Ort	New York
Period	2017/09/29 → 2017/10/02

Ämnesklassifikation (UKÄ)

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10.1109/UCC56403.2022.00040

DML_ICC_2022_ServiceMeshRL_short__No_template_

Autonomous datacenter for long term deployment: AutoDC
Eker, J. (Forskare), Årzén, K.-E. (Forskare) & Heimerson, A. (Forskare)
2018/12/01 → 2021/10/06
Projekt: Forskning
Händelsebaserad reglering av stokastiska system med tillämpning mot serversystem
Cervin, A. (PI), Thelander Andrén, M. (Forskarstuderande), Bernhardsson, B. (Biträdande handledare), Soltesz, K. (Biträdande handledare), Heimerson, A. (Forskarstuderande) & Ruuskanen, J. (Forskarstuderande)
Swedish Research Council
2018/01/01 → 2022/12/31
Projekt: Forskning

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@inproceedings{980385f4ff4b413baf01554c56552369,

title = "A Proactive Cloud Application Auto-Scaler using Reinforcement Learning",

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 = "Albin Heimerson and Johan Eker and Karl-Erik {\AA}rz{\'e}n",

year = "2022",

doi = "10.1109/UCC56403.2022.00040",

language = "English",

pages = "213--220",

booktitle = "UCC '22: Proceedings of the 15th IEEE/ACM International Conference on Utility and Cloud Computing",

note = "15th ACM-IEEE International Conference on Formal Methods and Models for System Design ; Conference date: 29-09-2017 Through 02-10-2017",

}

Heimerson, A , Eker, J & Årzén, K-E 2022, A Proactive Cloud Application Auto-Scaler using Reinforcement Learning. i UCC '22: Proceedings of the 15th IEEE/ACM International Conference on Utility and Cloud Computing. s. 213-220, 15th ACM-IEEE International Conference on Formal Methods and Models for System Design, New York, New York, USA, 2017/09/29. https://doi.org/10.1109/UCC56403.2022.00040

TY - GEN

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AU - Eker, Johan

AU - Årzén, Karl-Erik

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AB - 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.

U2 - 10.1109/UCC56403.2022.00040

DO - 10.1109/UCC56403.2022.00040

M3 - Paper in conference proceeding

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EP - 220

BT - UCC '22: Proceedings of the 15th IEEE/ACM International Conference on Utility and Cloud Computing

T2 - 15th ACM-IEEE International Conference on Formal Methods and Models for System Design

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ER -

A Proactive Cloud Application Auto-Scaler using Reinforcement Learning

Sammanfattning

Konferens

Ämnesklassifikation (UKÄ)

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Projekt

Autonomous datacenter for long term deployment: AutoDC

Händelsebaserad reglering av stokastiska system med tillämpning mot serversystem

Citera det här