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Why Is It Not Solved Yet?: Challenges for Production-Ready Autoscaling

Authors:

Martin Straesser,

Johannes Grohmann,

Jóakim von Kistowski,

Samuel KounevAuthors Info & Claims

ICPE '22: Proceedings of the 2022 ACM/SPEC on International Conference on Performance Engineering

Pages 105 - 115

https://doi.org/10.1145/3489525.3511680

Published: 09 April 2022 Publication History

Abstract

Autoscaling is a task of major importance in the cloud computing domain as it directly affects both operating costs and customer experience. Although there has been active research in this area for over ten years now, there is still a significant gap between the proposed methods in the literature and the deployed autoscalers in practice. Hence, many research autoscalers do not find their way into production deployments. This paper describes six core challenges that arise in production systems that are still not solved by most research autoscalers. We illustrate these problems through experiments in a realistic cloud environment with a real-world multi-service business application and show that commonly used autoscalers have various shortcomings. In addition, we analyze the behavior of overloaded services and show that these can be problematic for existing autoscalers. Generally, we analyze that these challenges are only insufficiently addressed in the literature and conclude that future scaling approaches should focus on the needs of production systems.

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Cited By

STAROVOYTOV AKRASNOPROSHIN V(2024)REAL-TIME MANAGEMENT OF CRITICAL IT-SYSTEMS BASED ON NEURAL NETWORK TECHNOLOGIESHERALD OF POLOTSK STATE UNIVERSITY. Series С FUNDAMENTAL SCIENCES10.52928/2070-1624-2024-42-1-18-25(18-25)Online publication date: 24-Apr-2024
https://doi.org/10.52928/2070-1624-2024-42-1-18-25
Zou DLu WZhu ZLu XZhou JWang XLiu KWang KSun RWang H(2024)OptScaler: A Collaborative Framework for Robust Autoscaling in the CloudProceedings of the VLDB Endowment10.14778/3685800.368582917:12(4090-4103)Online publication date: 8-Nov-2024
https://doi.org/10.14778/3685800.3685829
Raith PNastic SDustdar S(2024)SimuScale: Optimizing Parameters for Autoscaling of Serverless Edge Functions Through Co-Simulation2024 IEEE 17th International Conference on Cloud Computing (CLOUD)10.1109/CLOUD62652.2024.00042(305-315)Online publication date: 7-Jul-2024
https://doi.org/10.1109/CLOUD62652.2024.00042
Show More Cited By

Index Terms

Why Is It Not Solved Yet?: Challenges for Production-Ready Autoscaling
1. Computer systems organization
  1. Architectures
    1. Distributed architectures
      1. Cloud computing
2. Software and its engineering
  1. Software organization and properties
    1. Extra-functional properties
      1. Software performance

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Published In

cover image ACM Conferences

ICPE '22: Proceedings of the 2022 ACM/SPEC on International Conference on Performance Engineering

April 2022

242 pages

ISBN:9781450391436

DOI:10.1145/3489525

General Chairs:
Dan Feng
Huazhong University of Science and Technology, China
,
Steffen Becker
University of Stuttgart, Germany
,
Program Chairs:
Nikolas Herbst
University of Würzburg, Germany
,
Philipp Leitner
Chalmers and University of Gothenburg
,
Publications Chair:
Alessandro Papadopoulos
Mälardalen University, Sweden

Copyright © 2022 ACM.

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Published: 09 April 2022

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April 9 - 13, 2022

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ICPE '22 Paper Acceptance Rate 14 of 58 submissions, 24%;

Overall Acceptance Rate 252 of 851 submissions, 30%

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Cited By

STAROVOYTOV AKRASNOPROSHIN V(2024)REAL-TIME MANAGEMENT OF CRITICAL IT-SYSTEMS BASED ON NEURAL NETWORK TECHNOLOGIESHERALD OF POLOTSK STATE UNIVERSITY. Series С FUNDAMENTAL SCIENCES10.52928/2070-1624-2024-42-1-18-25(18-25)Online publication date: 24-Apr-2024
https://doi.org/10.52928/2070-1624-2024-42-1-18-25
Zou DLu WZhu ZLu XZhou JWang XLiu KWang KSun RWang H(2024)OptScaler: A Collaborative Framework for Robust Autoscaling in the CloudProceedings of the VLDB Endowment10.14778/3685800.368582917:12(4090-4103)Online publication date: 8-Nov-2024
https://doi.org/10.14778/3685800.3685829
Raith PNastic SDustdar S(2024)SimuScale: Optimizing Parameters for Autoscaling of Serverless Edge Functions Through Co-Simulation2024 IEEE 17th International Conference on Cloud Computing (CLOUD)10.1109/CLOUD62652.2024.00042(305-315)Online publication date: 7-Jul-2024
https://doi.org/10.1109/CLOUD62652.2024.00042
Pintye IKovács JLovas R(2024)Enhancing Machine Learning-Based Autoscaling for Cloud Resource OrchestrationJournal of Grid Computing10.1007/s10723-024-09783-122:4Online publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1007/s10723-024-09783-1
Galante Gda Rosa Righi Rde Andrade C(2024)Extending parallel programming patterns with adaptability featuresCluster Computing10.1007/s10586-024-04622-027:9(12547-12568)Online publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1007/s10586-024-04622-0
Straesser MHaas PFrank SHakamian Avan Hoorn AKounev S(2024)Kubernetes-in-the-Loop: Enriching Microservice Simulation Through Authentic Container OrchestrationPerformance Evaluation Methodologies and Tools10.1007/978-3-031-48885-6_6(82-98)Online publication date: 3-Jan-2024
https://doi.org/10.1007/978-3-031-48885-6_6
Straesser MGeißler SHoßfeld TKounev S(2023)Power to the Applications: The Vision of Continuous Decentralized Autoscaling2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing Workshops (CCGridW)10.1109/CCGridW59191.2023.00058(281-283)Online publication date: May-2023
https://doi.org/10.1109/CCGridW59191.2023.00058

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