{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,23]],"date-time":"2025-10-23T05:45:17Z","timestamp":1761198317638,"version":"build-2065373602"},"reference-count":33,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T00:00:00Z","timestamp":1761091200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Technology Research and Development Program of China","award":["2023YFB3905300","2023YFB3905302"],"award-info":[{"award-number":["2023YFB3905300","2023YFB3905302"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>Container-based virtualization has become pivotal in cloud computing, and resource fragmentation is inevitable due to the frequency of container deployment\/termination and the heterogeneous nature of IoT tasks. In queuing cloud systems, resource defragmentation and task scheduling are interdependent yet rarely co-optimized in existing research. This paper addresses this gap by investigating the joint optimization of resource defragmentation and task scheduling in a queuing cloud computing system. We first formulate the problem to minimize task completion time and maximize resource utilization, then transform it into an online decision problem. We propose a Deep Reinforcement Learning (DRL)-based two-layer iterative approach called DRL-RDG, which uses a Resource Defragmentation approach based on a Greedy strategy (RDG) to find the optimal container migration solution and a DRL algorithm to learn the optimal task-scheduling solution. Simulation results show that DRL-RDG achieves a low average task completion time and high resource utilization, demonstrating its effectiveness in queuing cloud environments.<\/jats:p>","DOI":"10.3390\/fi17110483","type":"journal-article","created":{"date-parts":[[2025,10,23]],"date-time":"2025-10-23T05:20:44Z","timestamp":1761196844000},"page":"483","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Joint Optimization of Container Resource Defragmentation and Task Scheduling in Queueing Cloud Computing: A DRL-Based Approach"],"prefix":"10.3390","volume":"17","author":[{"given":"Yan","family":"Guo","sequence":"first","affiliation":[{"name":"Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites, National Satellite Meteorological Center (National Center for Space Weather), China Meteorological Administration, Beijing 100081, China"},{"name":"Innovation Center for FengYun Meteorological Satellite (FYSIC), Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lan","family":"Wei","sequence":"additional","affiliation":[{"name":"Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites, National Satellite Meteorological Center (National Center for Space Weather), China Meteorological Administration, Beijing 100081, China"},{"name":"Innovation Center for FengYun Meteorological Satellite (FYSIC), Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cunqun","family":"Fan","sequence":"additional","affiliation":[{"name":"Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites, National Satellite Meteorological Center (National Center for Space Weather), China Meteorological Administration, Beijing 100081, China"},{"name":"Innovation Center for FengYun Meteorological Satellite (FYSIC), Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"You","family":"Ma","sequence":"additional","affiliation":[{"name":"Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites, National Satellite Meteorological Center (National Center for Space Weather), China Meteorological Administration, Beijing 100081, China"},{"name":"Innovation Center for FengYun Meteorological Satellite (FYSIC), Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiangang","family":"Zhao","sequence":"additional","affiliation":[{"name":"Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites, National Satellite Meteorological Center (National Center for Space Weather), China Meteorological Administration, Beijing 100081, China"},{"name":"Innovation Center for FengYun Meteorological Satellite (FYSIC), Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Henghong","family":"He","sequence":"additional","affiliation":[{"name":"Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites, National Satellite Meteorological Center (National Center for Space Weather), China Meteorological Administration, Beijing 100081, China"},{"name":"Innovation Center for FengYun Meteorological Satellite (FYSIC), Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,22]]},"reference":[{"key":"ref_1","first-page":"1","article-title":"A comprehensive survey on cloud computing: Architecture, tools, technologies, and open issues","volume":"12","author":"Jawed","year":"2022","journal-title":"Int. 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