{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T20:16:25Z","timestamp":1784837785325,"version":"3.55.0"},"reference-count":43,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Journal of Industrial Information Integration"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1016\/j.jii.2026.101154","type":"journal-article","created":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T00:11:05Z","timestamp":1781827865000},"page":"101154","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["A scalable deep learning framework for job completion time prediction in cloud-edge environments"],"prefix":"10.1016","volume":"52","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6761-0175","authenticated-orcid":false,"given":"Sicheng","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lingyan","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fujia","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Renzhe","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"15","key":"10.1016\/j.jii.2026.101154_b1","doi-asserted-by":"crossref","first-page":"12861","DOI":"10.1109\/JIOT.2021.3139827","article-title":"The duo of artificial intelligence and big data for industry 4.0: Applications, techniques, challenges, and future research directions","volume":"9","author":"Jagatheesaperumal","year":"2021","journal-title":"IEEE Internet Things J."},{"issue":"2","key":"10.1016\/j.jii.2026.101154_b2","first-page":"110","article-title":"Big data analytics, artificial intelligence, machine learning, internet of things, and blockchain for enhanced business intelligence","volume":"1","author":"Paramesha","year":"2024","journal-title":"Partners Univers. Multidiscip. Res. J."},{"issue":"9","key":"10.1016\/j.jii.2026.101154_b3","doi-asserted-by":"crossref","first-page":"2751","DOI":"10.1080\/00207543.2019.1602744","article-title":"Big data driven jobs remaining time prediction in discrete manufacturing system: a deep learning-based approach","volume":"58","author":"Fang","year":"2020","journal-title":"Int. J. Prod. Res."},{"key":"10.1016\/j.jii.2026.101154_b4","doi-asserted-by":"crossref","first-page":"1303","DOI":"10.1007\/s10845-017-1325-3","article-title":"Deep neural networks based order completion time prediction by using real-time job shop RFID data","volume":"30","author":"Wang","year":"2019","journal-title":"J. Intell. Manuf."},{"issue":"11","key":"10.1016\/j.jii.2026.101154_b5","doi-asserted-by":"crossref","first-page":"1027","DOI":"10.1515\/auto-2022-0089","article-title":"Machine learning with nonlinear state space models","volume":"70","author":"Sch\u00fcssler","year":"2022","journal-title":"At-Automatisierungstechnik"},{"issue":"1","key":"10.1016\/j.jii.2026.101154_b6","doi-asserted-by":"crossref","first-page":"1795","DOI":"10.1038\/s41598-024-84240-3","article-title":"Production scheduling with multi-robot task allocation in a real industry 4.0 setting","volume":"15","author":"Shakeri","year":"2025","journal-title":"Sci. Rep."},{"issue":"7","key":"10.1016\/j.jii.2026.101154_b7","doi-asserted-by":"crossref","first-page":"573","DOI":"10.3390\/machines10070573","article-title":"An end-to-end deep learning method for dynamic job shop scheduling problem","volume":"10","author":"Chen","year":"2022","journal-title":"Machines"},{"issue":"11","key":"10.1016\/j.jii.2026.101154_b8","doi-asserted-by":"crossref","first-page":"5313","DOI":"10.3390\/s23115313","article-title":"Di-CNN: Domain-knowledge-informed convolutional neural network for manufacturing quality prediction","volume":"23","author":"Guo","year":"2023","journal-title":"Sensors"},{"key":"10.1016\/j.jii.2026.101154_b9","doi-asserted-by":"crossref","DOI":"10.7717\/peerj-cs.1084","article-title":"An enhanced CNN-LSTM remaining useful life prediction model for aircraft engine with attention mechanism","volume":"8","author":"Li","year":"2022","journal-title":"PeerJ Comput. Sci."},{"issue":"2","key":"10.1016\/j.jii.2026.101154_b10","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1109\/TSM.2022.3164578","article-title":"Temporal convolution-based long-short term memory network with attention mechanism for remaining useful life prediction","volume":"35","author":"Hsu","year":"2022","journal-title":"IEEE Trans. Semicond. Manuf."},{"issue":"19","key":"10.1016\/j.jii.2026.101154_b11","doi-asserted-by":"crossref","first-page":"4187","DOI":"10.3390\/electronics12194187","article-title":"Digital twins temporal dependencies-based on time series using multivariate long short-term memory","volume":"12","author":"Isah","year":"2023","journal-title":"Electronics"},{"issue":"2","key":"10.1016\/j.jii.2026.101154_b12","doi-asserted-by":"crossref","first-page":"1322","DOI":"10.1109\/TII.2022.3167380","article-title":"A deep reinforcement learning framework based on an attention mechanism and disjunctive graph embedding for the job-shop scheduling problem","volume":"19","author":"Chen","year":"2022","journal-title":"IEEE Trans. Ind. Inform."},{"key":"10.1016\/j.jii.2026.101154_b13","doi-asserted-by":"crossref","DOI":"10.1109\/TNET.2024.3415089","article-title":"Optimizing task placement and online scheduling for distributed GNN training acceleration in heterogeneous systems","author":"Luo","year":"2024","journal-title":"IEEE\/ACM Trans. Netw."},{"issue":"11","key":"10.1016\/j.jii.2026.101154_b14","doi-asserted-by":"crossref","first-page":"3489","DOI":"10.1016\/j.cor.2007.01.026","article-title":"Real-time prediction of order flowtimes using support vector regression","volume":"35","author":"Alenezi","year":"2008","journal-title":"Comput. Oper. Res."},{"key":"10.1016\/j.jii.2026.101154_b15","doi-asserted-by":"crossref","first-page":"649","DOI":"10.1016\/j.procs.2021.01.287","article-title":"Prototyping machine-learning-supported lead time prediction using automl","volume":"180","author":"Bender","year":"2021","journal-title":"Procedia Comput. Sci."},{"key":"10.1016\/j.jii.2026.101154_b16","first-page":"269","article-title":"A learning analytics approach for job scheduling on cloud servers","author":"Gharajeh","year":"2017","journal-title":"Learn. Anal.: Fundam. Appl. Trends: A View Current State the Art To Enhanc. E-Learning"},{"issue":"9","key":"10.1016\/j.jii.2026.101154_b17","doi-asserted-by":"crossref","first-page":"1708","DOI":"10.3390\/e13091708","article-title":"An artificial bee colony algorithm for the job shop scheduling problem with random processing times","volume":"13","author":"Zhang","year":"2011","journal-title":"Entropy"},{"issue":"1","key":"10.1016\/j.jii.2026.101154_b18","doi-asserted-by":"crossref","first-page":"14175","DOI":"10.1038\/s41598-023-41295-y","article-title":"Time-feature attention-based convolutional auto-encoder for flight feature extraction","volume":"13","author":"Wang","year":"2023","journal-title":"Sci. Rep."},{"issue":"7","key":"10.1016\/j.jii.2026.101154_b19","doi-asserted-by":"crossref","first-page":"973","DOI":"10.3390\/math12070973","article-title":"Time series prediction based on multi-scale feature extraction","volume":"12","author":"Zhang","year":"2024","journal-title":"Mathematics"},{"key":"10.1016\/j.jii.2026.101154_b20","doi-asserted-by":"crossref","first-page":"126337","DOI":"10.1109\/ACCESS.2021.3111306","article-title":"An advanced LSTM model for optimal scheduling in smart logistic environment: E-commerce case","volume":"9","author":"Issaoui","year":"2021","journal-title":"IEEE Access"},{"issue":"9","key":"10.1016\/j.jii.2026.101154_b21","doi-asserted-by":"crossref","first-page":"4169","DOI":"10.1007\/s00170-023-12123-4","article-title":"Hierarchical ensemble deep learning for data-driven lead time prediction","volume":"128","author":"Aslan","year":"2023","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"10.1016\/j.jii.2026.101154_b22","doi-asserted-by":"crossref","first-page":"41334","DOI":"10.1109\/ACCESS.2021.3065391","article-title":"Optimal decision tree for cycle time prediction and allowance determination","volume":"9","author":"Hsu","year":"2021","journal-title":"IEEE Access"},{"key":"10.1016\/j.jii.2026.101154_b23","first-page":"385","article-title":"A hybrid framework integrating machine-learning and mathematical programming approaches for sustainable scheduling of flexible job-shop problems","volume":"103","author":"Li","year":"2023","journal-title":"Chem. Eng. Trans."},{"key":"10.1016\/j.jii.2026.101154_b24","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12859-019-3131-8","article-title":"Attention-based recurrent neural network for influenza epidemic prediction","volume":"20","author":"Zhu","year":"2019","journal-title":"BMC Bioinformatics"},{"key":"10.1016\/j.jii.2026.101154_b25","doi-asserted-by":"crossref","first-page":"103","DOI":"10.70470\/KHWARIZMIA\/2023\/010","article-title":"Efficient hardware acceleration techniques for deep learning on edge devices: A comprehensive performance analysis","volume":"2023","author":"Burhanuddin","year":"2023","journal-title":"Khwarizmia"},{"key":"10.1016\/j.jii.2026.101154_b26","doi-asserted-by":"crossref","first-page":"10","DOI":"10.70470\/SHIFRA\/2023\/002","article-title":"Enhancing IoT device security through blockchain technology: A decentralized approach","volume":"2023","author":"Al Barazanchi","year":"2023","journal-title":"Shifra"},{"key":"10.1016\/j.jii.2026.101154_b27","first-page":"1","article-title":"Integrated scheduling of distributed manufacturing with assembly and distribution: state of the art, challenges, and future directions","author":"Fu","year":"2025","journal-title":"Int. J. Prod. Res."},{"issue":"1","key":"10.1016\/j.jii.2026.101154_b28","article-title":"Deep learning for computer vision: A brief review","volume":"2018","author":"Voulodimos","year":"2018","journal-title":"Comput. Intell. Neurosci."},{"key":"10.1016\/j.jii.2026.101154_b29","doi-asserted-by":"crossref","DOI":"10.1016\/j.mex.2024.102946","article-title":"A critical review of RNN and LSTM variants in hydrological time series predictions","volume":"13","author":"Waqas","year":"2024","journal-title":"MethodsX"},{"key":"10.1016\/j.jii.2026.101154_b30","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.122807","article-title":"A comparison review of transfer learning and self-supervised learning: Definitions, applications, advantages and limitations","volume":"242","author":"Zhao","year":"2024","journal-title":"Expert Syst. Appl."},{"issue":"14","key":"10.1016\/j.jii.2026.101154_b31","doi-asserted-by":"crossref","first-page":"2257","DOI":"10.3390\/math13142257","article-title":"Survey on replay-based continual learning and empirical validation on feasibility in diverse edge devices using a representative method","volume":"13","author":"Park","year":"2025","journal-title":"Mathematics"},{"key":"10.1016\/j.jii.2026.101154_b32","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/JTEHM.2019.2952610","article-title":"A multi-task group Bi-LSTM networks application on electrocardiogram classification","volume":"8","author":"Lv","year":"2019","journal-title":"IEEE J. Transl. Eng. Health Med."},{"key":"10.1016\/j.jii.2026.101154_b33","doi-asserted-by":"crossref","first-page":"340","DOI":"10.1016\/j.neucom.2019.10.068","article-title":"Attention augmentation with multi-residual in bidirectional LSTM","volume":"385","author":"Wang","year":"2020","journal-title":"Neurocomputing"},{"key":"10.1016\/j.jii.2026.101154_b34","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1016\/j.ins.2021.06.009","article-title":"An intelligent computer-aided approach for atrial fibrillation and atrial flutter signals classification using modified bidirectional LSTM network","volume":"574","author":"Wang","year":"2021","journal-title":"Inform. Sci."},{"key":"10.1016\/j.jii.2026.101154_b35","doi-asserted-by":"crossref","DOI":"10.1109\/TSUSC.2025.3528105","article-title":"Serving transformer models via joint requst scheduling and batching in the network edge","author":"Fu","year":"2025","journal-title":"IEEE Trans. Sustain. Comput."},{"key":"10.1016\/j.jii.2026.101154_b36","article-title":"A bidirectional gated recurrent unit and temporal convolutional network with a self-attention mechanism to improve traffic flow prediction performance","author":"Liu","year":"2025","journal-title":"IEEE Access"},{"key":"10.1016\/j.jii.2026.101154_b37","doi-asserted-by":"crossref","DOI":"10.1016\/j.array.2023.100280","article-title":"An efficient ACO-based algorithm for task scheduling in heterogeneous multiprocessing environments","volume":"17","author":"Elcock","year":"2023","journal-title":"Array"},{"issue":"03","key":"10.1016\/j.jii.2026.101154_b38","first-page":"3289","article-title":"Task scheduling optimization in cloud computing based on genetic algorithms","volume":"69","author":"Hamed","year":"2021","journal-title":"Comput. Mater. Contin"},{"issue":"1","key":"10.1016\/j.jii.2026.101154_b39","doi-asserted-by":"crossref","DOI":"10.1080\/23311916.2024.2328355","article-title":"Optimizing task scheduling in cloud computing: a hybrid artificial intelligence approach","volume":"11","author":"Alla","year":"2024","journal-title":"Cogent Eng."},{"issue":"3","key":"10.1016\/j.jii.2026.101154_b40","doi-asserted-by":"crossref","first-page":"920","DOI":"10.3390\/s22030920","article-title":"AdPSO: adaptive PSO-based task scheduling approach for cloud computing","volume":"22","author":"Nabi","year":"2022","journal-title":"Sensors"},{"issue":"4","key":"10.1016\/j.jii.2026.101154_b41","doi-asserted-by":"crossref","first-page":"257","DOI":"10.23919\/CSMS.2021.0027","article-title":"A review of reinforcement learning based intelligent optimization for manufacturing scheduling","volume":"1","author":"Wang","year":"2021","journal-title":"Complex Syst. Model. Simul."},{"key":"10.1016\/j.jii.2026.101154_b42","series-title":"Learning to generalize dispatching rules on the job shop scheduling","author":"Iklassov","year":"2022"},{"key":"10.1016\/j.jii.2026.101154_b43","series-title":"2024 Joint 13th International Conference on Soft Computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems","first-page":"1","article-title":"Optimizing job shop scheduling with deep Q-network for robust performance","author":"Yu-Cheng","year":"2024"}],"container-title":["Journal of Industrial Information Integration"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2452414X26000968?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2452414X26000968?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T19:51:58Z","timestamp":1784836318000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2452414X26000968"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":43,"alternative-id":["S2452414X26000968"],"URL":"https:\/\/doi.org\/10.1016\/j.jii.2026.101154","relation":{},"ISSN":["2452-414X"],"issn-type":[{"value":"2452-414X","type":"print"}],"subject":[],"published":{"date-parts":[[2026,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A scalable deep learning framework for job completion time prediction in cloud-edge environments","name":"articletitle","label":"Article Title"},{"value":"Journal of Industrial Information Integration","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.jii.2026.101154","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"101154"}}