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AuthorAbdullah, Muhammad
AuthorIqbal, Waheed
AuthorBukhari, Faisal
AuthorErradi, Abdelkarim
Available date2023-04-10T09:10:04Z
Publication Date2020
Publication NameIEEE Transactions on Network and Service Management
ResourceScopus
URIhttp://dx.doi.org/10.1109/TNSM.2020.3033025
URIhttp://hdl.handle.net/10576/41798
AbstractContainers provide a lightweight runtime environment for microservices applications while enabling better server utilization. Automatic optimal allocation of CPU pins to the containers serving specific workloads can help to minimize the completion time of jobs. Most of the existing state-of-the-art focused on building new efficient scheduling algorithms for placing the containers on the infrastructure, and the resources to the containers are allocated manually and statically. An automatic method to identify and allocate optimal CPU resources to the containers can help to improve the efficiency of the scheduling algorithms. In this article, we introduce a new deep learning-based approach to allocate optimal CPU resources to the containers automatically. Our approach uses the law of diminishing marginal returns to determine the optimal number of CPU pins for containers to gain maximum performance while maximizing the number of concurrent jobs. The proposed method is evaluated using real workloads on a Docker-based containerized infrastructure. The results demonstrate the effectiveness of the proposed solution in reducing the completion time of the jobs by 23% to 74% compared to commonly used static CPU allocation methods. 2004-2012 IEEE.
SponsorManuscript received April 30, 2020; revised September 28, 2020; accepted October 19, 2020. Date of publication October 22, 2020; date of current version December 9, 2020. This publication was made possible by NPRP Grant # NPRP9-224-1-049 from the Qatar National Research Fund (a member of Qatar Foundation) and a graduate fellowship from the Higher Education Commission (HEC) of Pakistan to MA. The associate editor coordinating the review of this article and approving it for publication was T. Inoue. (Corresponding author: Waheed Iqbal.) Muhammad Abdullah, Waheed Iqbal, and Faisal Bukhari are with the Punjab University College of Information Technology, University of the Punjab, Lahore 54590, Pakistan (e-mail: muhammad.abdullah@pucit.edu.pk; waheed.iqbal@pucit.edu.pk; faisal.bukhari@pucit.edu.pk).
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
SubjectContainers
CPU allocation
CPU pin
diminishing marginal
job completion time
TitleDiminishing Returns and Deep Learning for Adaptive CPU Resource Allocation of Containers
TypeArticle
Pagination2052-2063
Issue Number4
Volume Number17
dc.accessType Abstract Only


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