Self-clearing Strategy of Container Image in High Performance Job Scheduling
Authors: 
Kehao Li, Hongbo Gao, Bo Tang, Ke YangAuthors Info & Claims
Kehao Li, Hongbo Gao, Bo Tang, Ke YangAuthors Info & ClaimsPages 92 - 97
Abstract
Container based service deployment, operation and maintenance are widely used with the advent of the container technology. As an actual industry standard, image allows applications and their dependencies and execution environment to be packaged in a lightweight, self-contained container, which can easily realize the sharing of environment and resources among developers, providing a basic guarantee for service containerization. In practical applications, with the increasing number of deployments, versions of business container images often need to be frequently updated, resulting in the continuous accumulation of images on various nodes in the cluster and occupying massive storage capacity. It causes additional overhead of hardware and other resources and affects the computing performance of nodes. In this paper, we propose an image clearing strategy in the production environment of containerized Kubernetes cluster. In this paper, we present the analysis, design and evaluation of the image distribution of each node in the cluster by introducing a distributed cluster system model, based on which four image clearing methods are proposed to realize the autonomous deletion of redundant images while reducing the influence on the current running environment.
References
[1]
[1] T. Harter, B. Salmon, and R. Liu, “Slacker: fast distribution with lazy docker containers,” 14th Usenix Conference on File and Storage Technologies, pp. 181–195, 2016.
[2]
[2] Y. Deng, F. Wang, “Exploring the performance impact of stripe size on network attached storage systems”, Journal of Systems Architecture, vol. 54(8), pp. 787–796, 2008.
[3]
[3] R. Dua, V. Kohli, S. Patil, “Performance analysis of Union and CoW file systems with Docker,” Computing, Analytics and Security Trends(CAST), International Conference on, IEEE, pp. 550–555, 2016.
[4]
[4] Y. Zhou, Y. H. Deng, “Reduction Strategy for Docker Images Based on Probabilistic Model,” Journal of Chinese Computer System, vol. 39(9), pp. 1908–1913.
[5]
[5] P. Geng, W. Chen, J. Wei, “Tool for building dockers image on dockerfile,” Computer System & Applications, vol. 25(11), pp. 14–21, 2016.
[6]
[6] Q. Z. Yang, L. Liu, K. S. Qin, “A method, system, terminal and storage medium for clearing images with the same based on Docker,” CN 111752676, 10.09.2020.
[7]
[7] Y. Wang, H, Kadiyala, J. Rubin, “Promises and challenges of microservices: an exploratory study,” Empirical Software Engineering, vol. 26(4), pp. 1–44, 2021.
[8]
[8] D. Skourtis, L. Rupprecht, V. Tarasov, “Carving perfect layers out of docker images,” 11th Usenix Workshop on Hot Topics in Cloud Computing (HotCloud 19), 2019.
[9]
[9] L. Gu, D. Zeng, et.al, “Exploring layered container structure for cost efficient microservice deployment,” IEEE INFOCOM 2021-IEEE Conference on Computer Communications. IEEE, pp. 1–9, 2021.
[10]
[10] L. Funari, L. Petrucci, A. Detti, “Storage-saving scheduling policies for clusters running containers,” IEEE Trans. on Cloud Computing, 2021.
[11]
[11] U. Maheshwari, B. H. Liskov, “Fault-tolerant distributed garbage collection in a client-server object-oriented database,” Proceedings of 3rd International Conference on Parallel and Distributed Information Systems. IEEE, pp. 239-248, 1994.
[12]
[12] J. M. Wu, Z. H. Song, G. L. Chen, “RRDGC: A Distributed Garbage Collection Algorithm on Relaxed Consistency Model,” Mini-Micro Systems, vol. 27(7), pp. 1304-1307, 2006.
[13]
[13] D. Kimovski, A. Marosi, S. Gec, N. Saurabh, A. Kertesz, G. Kecskemeti, V. Stankovski, R. Prodan, "Distributed environment for efficient virtual machine image management in federated cloud architectures," Concurrency and Computation: Practice and Experience, vol. 30, no. 20, pp. e4220, 2018, Wiley Online Library.
[14]
[14] “Kubernetes Scheduler,” Accessed on: Jan 10, 2022, [Online]. Available: https://kubernetes.io/docs/concepts/scheduling-eviction/kube-scheduler/
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Published In
November 2023
266 pages
ISBN:9798400716300
DOI:10.1145/3637494
Copyright © 2023 ACM.
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Association for Computing Machinery
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Published: 05 February 2024
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CECCT 2023
CECCT 2023: 2023 International Conference on Electronics, Computers and Communication Technology
November 17 - 19, 2023
Guilin, China
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