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A Dynamic I/O Sensing Scheduling Scheme in Kubernetes

Published: 06 August 2020 Publication History

Abstract

With the rapid development of the Container-as-a-Service (CaaS), Kubernetes has become the de facto standard for deploying containerized applications on cloud environments. However, the Kubernetes scheduler does not take the disk I/O load of nodes into account, which leads two problems: (1) Multiple I/O-intensive applications may be dispatched to the same node, which cause I/O bottlenecks. (2) Pods are less likely to be scheduled on node with idle I/O and insufficient CPU, resulting in the waste of the node's I/O resource. To address these problems, we first propose a dynamic scheduling algorithm named by Balanced-Disk-IO-Priority (BDI) to improve the disk I/O balance between the nodes. Moreover, we also propose a dynamic scheduling algorithm called Balanced-CPU-Disk-IO-Priority (BCDI) to solve the issue of load imbalance of CPU and disk I/O on single node. The experimental results show that the BDI algorithm and BCDI algorithm are more effective than the Kubernetes default scheduling algorithms.

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Cited By

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  • (2024)ComboFunc: Joint Resource Combination and Container Placement for Serverless Function Scaling with Heterogeneous ContainerIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2024.3454071(1-17)Online publication date: 2024
  • (2023)Algorithm Design for Kubernetes Load Saturation Scheduling in Deep Learning2023 IEEE 6th International Conference on Automation, Electronics and Electrical Engineering (AUTEEE)10.1109/AUTEEE60196.2023.10408382(1014-1019)Online publication date: 15-Dec-2023
  • (2023)Predicting resource consumption of Kubernetes container systems using resource modelsJournal of Systems and Software10.1016/j.jss.2023.111750203:COnline publication date: 13-Jul-2023
  • Show More Cited By

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cover image ACM Other conferences
HP3C 2020: Proceedings of the 2020 4th International Conference on High Performance Compilation, Computing and Communications
June 2020
191 pages
ISBN:9781450376914
DOI:10.1145/3407947
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

In-Cooperation

  • Xi'an Jiaotong-Liverpool University: Xi'an Jiaotong-Liverpool University
  • City University of Hong Kong: City University of Hong Kong
  • Guangdong University of Technology: Guangdong University of Technology

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 August 2020

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Author Tags

  1. Cloud Computing
  2. I/O Balancing
  3. Kubernetes
  4. Scheduling

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Cited By

View all
  • (2024)ComboFunc: Joint Resource Combination and Container Placement for Serverless Function Scaling with Heterogeneous ContainerIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2024.3454071(1-17)Online publication date: 2024
  • (2023)Algorithm Design for Kubernetes Load Saturation Scheduling in Deep Learning2023 IEEE 6th International Conference on Automation, Electronics and Electrical Engineering (AUTEEE)10.1109/AUTEEE60196.2023.10408382(1014-1019)Online publication date: 15-Dec-2023
  • (2023)Predicting resource consumption of Kubernetes container systems using resource modelsJournal of Systems and Software10.1016/j.jss.2023.111750203:COnline publication date: 13-Jul-2023
  • (2022)Custom Scheduling in Kubernetes: A Survey on Common Problems and Solution ApproachesACM Computing Surveys10.1145/354478855:7(1-37)Online publication date: 15-Dec-2022
  • (2022)Cloud Native Virtual Computing Cluster2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS)10.1109/CCIS57298.2022.10016430(273-277)Online publication date: 26-Nov-2022
  • (2021)Research and Implementation of Scheduling Strategy in Kubernetes for Computer Science Laboratory in UniversitiesInformation10.3390/info1201001612:1(16)Online publication date: 3-Jan-2021
  • (2021)Boreas – A Service Scheduler for Optimal Kubernetes DeploymentService-Oriented Computing10.1007/978-3-030-91431-8_14(221-237)Online publication date: 18-Nov-2021

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