{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T09:39:11Z","timestamp":1766137151312,"version":"3.37.3"},"reference-count":27,"publisher":"World Scientific Pub Co Pte Ltd","issue":"13","funder":[{"name":"the Jilin Provincial Industrial Innovation Special Foundation","award":["2017C028-4"],"award-info":[{"award-number":["2017C028-4"]}]},{"name":"the Jilin Province Science and Technology Development Plan Project \"Big Data Intelligent Vehicle Networking Service Platform\"","award":["20180623005TC"],"award-info":[{"award-number":["20180623005TC"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Patt. Recogn. Artif. Intell."],"published-print":{"date-parts":[[2019,12,15]]},"abstract":"<jats:p> Large-scale tasks processing based on cloud computing has become crucial to big data analysis and disposal in recent years. Most previous work, generally, utilize the conventional methods and architectures for general scale tasks to achieve tons of tasks disposing, which is limited by the issues of computing capability, data transmission, etc. Based on this argument, a fat-tree structure-based approach called LTDR (Large-scale Tasks processing using Deep network model and Reinforcement learning) has been proposed in this work. Aiming at exploring the optimal task allocation scheme, a virtual network mapping algorithm based on deep convolutional neural network and [Formula: see text]-learning is presented herein. After feature extraction, we design and implement a policy network to make node mapping decisions. The link mapping scheme can be attained by the designed distributed value-function based reinforcement learning model. Eventually, tasks are allocated onto proper physical nodes and processed efficiently. Experimental results show that LTDR can significantly improve the utilization of physical resources and long-term revenue while satisfying task requirements in big data. <\/jats:p>","DOI":"10.1142\/s0218001419510108","type":"journal-article","created":{"date-parts":[[2019,3,3]],"date-time":"2019-03-03T23:19:16Z","timestamp":1551655156000},"page":"1951010","source":"Crossref","is-referenced-by-count":13,"title":["Explore Deep Neural Network and Reinforcement Learning to Large-scale Tasks Processing in Big Data"],"prefix":"10.1142","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2186-3433","authenticated-orcid":false,"given":"Chunyi","family":"Wu","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, Jilin University, Changchun 130012, P. R. China"},{"name":"Jilin Province S&T Innovation Center for Physical Simulation and Security of Water Resources and Electric Power Engineering, Changchun Institute of Technology, Changchun 130012, P. R. China"}]},{"given":"Gaochao","family":"Xu","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Jilin University, Changchun 130012, P. R. China"}]},{"given":"Yan","family":"Ding","sequence":"additional","affiliation":[{"name":"Jilin Province S&T Innovation Center for Physical Simulation and Security of Water Resources and Electric Power Engineering, Changchun Institute of Technology, Changchun 130012, P. R. China"}]},{"given":"Jia","family":"Zhao","sequence":"additional","affiliation":[{"name":"Jilin Province S&T Innovation Center for Physical Simulation and Security of Water Resources and Electric Power Engineering, Changchun Institute of Technology, Changchun 130012, P. R. 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