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المؤلفAbdullah, Muhammad
المؤلفIqbal, Waheed
المؤلفBukhari, Faisal
المؤلفErradi, Abdelkarim
تاريخ الإتاحة2023-04-10T09:10:04Z
تاريخ النشر2020
اسم المنشورIEEE Transactions on Network and Service Management
المصدرScopus
معرّف المصادر الموحدhttp://dx.doi.org/10.1109/TNSM.2020.3033025
معرّف المصادر الموحدhttp://hdl.handle.net/10576/41798
الملخصContainers 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.
راعي المشروعManuscript 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).
اللغةen
الناشرInstitute of Electrical and Electronics Engineers Inc.
الموضوعContainers
CPU allocation
CPU pin
diminishing marginal
job completion time
العنوانDiminishing Returns and Deep Learning for Adaptive CPU Resource Allocation of Containers
النوعArticle
الصفحات2052-2063
رقم العدد4
رقم المجلد17
dc.accessType Abstract Only


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