short-paper

Adaptive application scheduling under interference in Kubernetes

Authors:

Víctor Medel,

Omer Rana,

José Ángel Bañares,

Unai ArronateguiAuthors Info & Claims

UCC '16: Proceedings of the 9th International Conference on Utility and Cloud Computing

Pages 426 - 427

https://doi.org/10.1145/2996890.3007889

Published: 06 December 2016 Publication History

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Abstract

Containers are rapidly replacing Virtual Machines (VMs) as the compute instance in cloud-based deployments. The significantly lower overhead of deploying containers (compared to VMs) has often been cited as one reason for this. However, interference caused by the limited isolation in shared resources can impact into the performance of hosted applications. We develop a Reference Net-based model of resource management within Kubernetes, primarily to better characterise such performance issues. Our model makes use of data obtained from a Kubernetes deployment, and can be used as a basis to design scalable (and potentially interference-tolerant) applications that make use of Kubernetes.

References

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Wen HCao ZLi BDu DAbouelwafa AVoigt DLiu SDiehl JWu F(2023)K8sES: Optimizing Kubernetes with Enhanced Storage Service-Level Objectives2023 IEEE 41st International Conference on Computer Design (ICCD)10.1109/ICCD58817.2023.00041(214-222)Online publication date: 6-Nov-2023
https://doi.org/10.1109/ICCD58817.2023.00041
Zhang WKosta SMogensen P(2023)A Scheduling Method for Tasks and Services in IIoT Multi-Cloud Environments2023 19th International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT)10.1109/DCOSS-IoT58021.2023.00056(293-300)Online publication date: Jun-2023
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Mummana SSambana BRamanababu BGandreti PRani PMishra PChandrasekhar ANarasimha Raju K(2023)A Microservice Architecture with Load Balancing Mechanism in Cloud EnvironmentMachine Learning and Big Data Analytics10.1007/978-3-031-15175-0_7(75-91)Online publication date: 7-Jun-2023
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Adaptive application scheduling under interference in Kubernetes
1. General and reference
  1. Cross-computing tools and techniques

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Information

Published In

UCC '16: Proceedings of the 9th International Conference on Utility and Cloud Computing

December 2016

549 pages

ISBN:9781450346160

DOI:10.1145/2996890

General Chairs:
Changjun Jiang
Tongji University, China
,
Omer Rana
Cardiff University, UK
,
Nick Antonopoulos
University of Derby, UK

Publication rights licensed to ACM. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 December 2016

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Ministerio de Economía y Competitividad

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UCC '16

UCC '16: 9th International Conference on Utility and Cloud Computing

December 6 - 9, 2016

Shanghai, China

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Overall Acceptance Rate 38 of 125 submissions, 30%

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Wen HCao ZLi BDu DAbouelwafa AVoigt DLiu SDiehl JWu F(2023)K8sES: Optimizing Kubernetes with Enhanced Storage Service-Level Objectives2023 IEEE 41st International Conference on Computer Design (ICCD)10.1109/ICCD58817.2023.00041(214-222)Online publication date: 6-Nov-2023
https://doi.org/10.1109/ICCD58817.2023.00041
Zhang WKosta SMogensen P(2023)A Scheduling Method for Tasks and Services in IIoT Multi-Cloud Environments2023 19th International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT)10.1109/DCOSS-IoT58021.2023.00056(293-300)Online publication date: Jun-2023
https://doi.org/10.1109/DCOSS-IoT58021.2023.00056
Mummana SSambana BRamanababu BGandreti PRani PMishra PChandrasekhar ANarasimha Raju K(2023)A Microservice Architecture with Load Balancing Mechanism in Cloud EnvironmentMachine Learning and Big Data Analytics10.1007/978-3-031-15175-0_7(75-91)Online publication date: 7-Jun-2023
https://doi.org/10.1007/978-3-031-15175-0_7
Zhu LLi JLiu ZZhang D(2022)A multi-resource scheduling scheme of Kubernetes for IIoTJournal of Systems Engineering and Electronics10.23919/JSEE.2022.00006333:3(683-692)Online publication date: Jun-2022
https://doi.org/10.23919/JSEE.2022.000063
Nguyen NKim T(2021)Balanced Leader Distribution Algorithm in Kubernetes ClustersSensors10.3390/s2103086921:3(869)Online publication date: 28-Jan-2021
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Kim ELee KYoo C(2021)On the Resource Management of Kubernetes2021 International Conference on Information Networking (ICOIN)10.1109/ICOIN50884.2021.9333977(154-158)Online publication date: 13-Jan-2021
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Zhang XLi LWang YChen EShou L(2021)Zeus: Improving Resource Efficiency via Workload Colocation for Massive Kubernetes ClustersIEEE Access10.1109/ACCESS.2021.31000829(105192-105204)Online publication date: 2021
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Kaur KGarg SKaddoum GAhmed SAtiquzzaman M(2020)KEIDS: Kubernetes-Based Energy and Interference Driven Scheduler for Industrial IoT in Edge-Cloud EcosystemIEEE Internet of Things Journal10.1109/JIOT.2019.29395347:5(4228-4237)Online publication date: May-2020
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Modelling performance & resource management in kubernetes

Scheduler activations for interference-resilient SMP virtual machine scheduling

Batch scheduling of consolidated virtual machines based on their workload interference model

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