{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,16]],"date-time":"2024-09-16T16:21:19Z","timestamp":1726503679713},"reference-count":41,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2021,8,1]],"date-time":"2021-08-01T00:00:00Z","timestamp":1627776000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2021,8,1]],"date-time":"2021-08-01T00:00:00Z","timestamp":1627776000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2021,8,1]],"date-time":"2021-08-01T00:00:00Z","timestamp":1627776000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2021,8,1]],"date-time":"2021-08-01T00:00:00Z","timestamp":1627776000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2021,8,1]],"date-time":"2021-08-01T00:00:00Z","timestamp":1627776000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,8,1]],"date-time":"2021-08-01T00:00:00Z","timestamp":1627776000000},"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":["Physical Communication"],"published-print":{"date-parts":[[2021,8]]},"DOI":"10.1016\/j.phycom.2021.101397","type":"journal-article","created":{"date-parts":[[2021,6,17]],"date-time":"2021-06-17T05:32:46Z","timestamp":1623907966000},"page":"101397","update-policy":"http:\/\/dx.doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":18,"special_numbering":"C","title":["A multi-user service migration scheme based on deep reinforcement learning and SDN in mobile edge computing"],"prefix":"10.1016","volume":"47","author":[{"given":"Wen","family":"Chen","sequence":"first","affiliation":[]},{"given":"Yuhu","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Jiaxing","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Zhangbin","family":"Tang","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"issue":"12","key":"10.1016\/j.phycom.2021.101397_b1","doi-asserted-by":"crossref","first-page":"5610","DOI":"10.1109\/TVT.2015.2480004","article-title":"Toward vehicle-assisted cloud computing for smartphones","volume":"64","author":"Zhang","year":"2015","journal-title":"IEEE Trans. Veh. Technol."},{"doi-asserted-by":"crossref","unstructured":"E. Yigitoglu, M. Mohamed, L. Liu, H. Ludwig, Foggy: A Framework for Continuous Automated IoT Application Deployment in Fog Computing, in: 2017 IEEE International Conference on AI Mobile Services (AIMS), 2017, pp. 38-45.","key":"10.1016\/j.phycom.2021.101397_b2","DOI":"10.1109\/AIMS.2017.14"},{"issue":"1","key":"10.1016\/j.phycom.2021.101397_b3","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1109\/JIOT.2016.2628938","article-title":"Enabling IoT for in-home rehabilitation: Accelerometer signals classification methods for activity and movement recognition","volume":"4","author":"Bisio","year":"2016","journal-title":"IEEE Internet Things J."},{"issue":"4","key":"10.1016\/j.phycom.2021.101397_b4","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1145\/1721654.1721672","article-title":"A view of cloud computing","volume":"53","author":"Armbrust","year":"2010","journal-title":"Commun. ACM"},{"issue":"4","key":"10.1016\/j.phycom.2021.101397_b5","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1109\/JIOT.2014.2327587","article-title":"Connected vehicles: Solutions and challenges","volume":"1","author":"Lu","year":"2014","journal-title":"IEEE Internet Things J."},{"key":"10.1016\/j.phycom.2021.101397_b6","first-page":"1","article-title":"V2VR: Reliable hybrid-network-oriented V2V data transmission and routing considering RSUs and connectivity probability","author":"Gao","year":"2020","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"issue":"8","key":"10.1016\/j.phycom.2021.101397_b7","doi-asserted-by":"crossref","first-page":"3983","DOI":"10.1109\/TVT.2013.2260188","article-title":"Conjecture-based load balancing for delay-sensitive users without message exchanges","volume":"62","author":"Shiang","year":"2013","journal-title":"IEEE Trans. Veh. Technol."},{"issue":"1","key":"10.1016\/j.phycom.2021.101397_b8","doi-asserted-by":"crossref","first-page":"450","DOI":"10.1109\/JIOT.2017.2750180","article-title":"Mobile edge computing: A survey","volume":"5","author":"Abbas","year":"2018","journal-title":"IEEE Internet Things J."},{"issue":"4","key":"10.1016\/j.phycom.2021.101397_b9","doi-asserted-by":"crossref","first-page":"2322","DOI":"10.1109\/COMST.2017.2745201","article-title":"A survey on mobile edge computing: The communication perspective","volume":"19","author":"Mao","year":"2017","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"10.1016\/j.phycom.2021.101397_b10","article-title":"Adaptive VM handoff across cloudlets","author":"K.\u00a0Ha","year":"2015","journal-title":"Tech"},{"key":"10.1016\/j.phycom.2021.101397_b11","series-title":"Proceedings of the 22nd Annual International Conference on Mobile Computing and Networking","first-page":"435","article-title":"Migrating running applications across mobile edge clouds: Poster","author":"Machen","year":"2016"},{"key":"10.1016\/j.phycom.2021.101397_b12","doi-asserted-by":"crossref","first-page":"23511","DOI":"10.1109\/ACCESS.2018.2828102","article-title":"A survey on service migration in mobile edge computing","volume":"6","author":"Wang","year":"2018","journal-title":"IEEE Access"},{"doi-asserted-by":"crossref","unstructured":"W. Felter, A. Ferreira, R. Rajamony, J. Rubio, An updated performance comparison of virtual machines and Linux containers, in: 2015 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS), 2015, pp. 171-172.","key":"10.1016\/j.phycom.2021.101397_b13","DOI":"10.1109\/ISPASS.2015.7095802"},{"issue":"5","key":"10.1016\/j.phycom.2021.101397_b14","doi-asserted-by":"crossref","first-page":"712","DOI":"10.1109\/TSC.2018.2827070","article-title":"Migration modeling and learning algorithms for containers in fog computing","volume":"12","author":"Tang","year":"2019","journal-title":"IEEE Trans. Serv. Comput."},{"year":"2011","author":"Zhai","series-title":"A DHT and MDP-based mobility management scheme for large-scale mobile internet","key":"10.1016\/j.phycom.2021.101397_b15"},{"issue":"1","key":"10.1016\/j.phycom.2021.101397_b16","doi-asserted-by":"crossref","first-page":"25","DOI":"10.14257\/ijgdc.2016.9.1.03","article-title":"Research on load balance method in SDN","volume":"9","author":"Chenxiao","year":"2016","journal-title":"Int. J. Grid Distributed Comput."},{"issue":"4","key":"10.1016\/j.phycom.2021.101397_b17","doi-asserted-by":"crossref","first-page":"1136","DOI":"10.1109\/TCCN.2020.3027681","article-title":"QoS prediction for service recommendation with features learning in mobile edge computing environment","volume":"6","author":"Yin","year":"2020","journal-title":"IEEE Trans. Cogn. Commun. Netw."},{"issue":"2","key":"10.1016\/j.phycom.2021.101397_b18","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1109\/MNET.2018.1700293","article-title":"A machine learning framework for resource allocation assisted by cloud computing","volume":"32","author":"Wang","year":"2018","journal-title":"IEEE Netw."},{"issue":"9","key":"10.1016\/j.phycom.2021.101397_b19","doi-asserted-by":"crossref","first-page":"6103","DOI":"10.1109\/TII.2020.2974875","article-title":"Dynamical resource allocation in edge for trustable internet-of-things systems: a reinforcement learning method","volume":"16","author":"Deng","year":"2020","journal-title":"IEEE Trans. Ind. Inf."},{"doi-asserted-by":"crossref","unstructured":"A. Ksentini, T. Taleb, M. Chen, A Markov Decision Process-based service migration procedure for follow me cloud, in: 2014 IEEE International Conference on Communications (ICC), 2014, pp. 1350-1354.","key":"10.1016\/j.phycom.2021.101397_b20","DOI":"10.1109\/ICC.2014.6883509"},{"doi-asserted-by":"crossref","unstructured":"S. Wang, R. Urgaonkar, T. He, M. Zafer, K. Chan, K.K. Leung, Mobility-Induced Service Migration in Mobile Micro-clouds, in: 2014 IEEE Military Communications Conference, 2014, pp. 835-840.","key":"10.1016\/j.phycom.2021.101397_b21","DOI":"10.1109\/MILCOM.2014.145"},{"doi-asserted-by":"crossref","unstructured":"T. Taleb, A. Ksentini, An analytical model for Follow Me Cloud, in: 2013 IEEE Global Communications Conference (GLOBECOM), 2013, pp. 1291-1296.","key":"10.1016\/j.phycom.2021.101397_b22","DOI":"10.1109\/GLOCOM.2013.6831252"},{"issue":"2","key":"10.1016\/j.phycom.2021.101397_b23","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1109\/TCC.2016.2525987","article-title":"Follow-me cloud: When cloud services follow mobile users","volume":"7","author":"Taleb","year":"2019","journal-title":"IEEE Trans. Cloud Comput."},{"doi-asserted-by":"crossref","unstructured":"S. Cao, Y. Wang, C. Xu, Service Migrations in the Cloud for Mobile Accesses: A Reinforcement Learning Approach, in: 2017 International Conference on Networking, Architecture, and Storage (NAS), 2017, pp. 1-10.","key":"10.1016\/j.phycom.2021.101397_b24","DOI":"10.1109\/NAS.2017.8026876"},{"key":"10.1016\/j.phycom.2021.101397_b25","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.future.2019.01.059","article-title":"Task migration for mobile edge computing using deep reinforcement learning","volume":"96","author":"Zhang","year":"2019","journal-title":"Future Gener. Comput. Syst."},{"doi-asserted-by":"crossref","unstructured":"Z. Gao, Q. Jiao, K. Xiao, Q. Wang, Z. Mo, Y. Yang, Deep Reinforcement Learning Based Service Migration Strategy for Edge Computing, in: 2019 IEEE International Conference on Service-Oriented System Engineering (SOSE), 2019, pp. 116-1165.","key":"10.1016\/j.phycom.2021.101397_b26","DOI":"10.1109\/SOSE.2019.00025"},{"issue":"5","key":"10.1016\/j.phycom.2021.101397_b27","doi-asserted-by":"crossref","first-page":"712","DOI":"10.1109\/TSC.2018.2827070","article-title":"Migration modeling and learning algorithms for containers in fog computing","volume":"12","author":"Tang","year":"2019","journal-title":"IEEE Trans. Serv. Comput."},{"key":"10.1016\/j.phycom.2021.101397_b28","first-page":"1","article-title":"Smart resource allocation for mobile edge computing: A deep reinforcement learning approach","author":"Wang","year":"2019","journal-title":"IEEE Trans. Emerg. Top. Comput."},{"issue":"6","key":"10.1016\/j.phycom.2021.101397_b29","doi-asserted-by":"crossref","first-page":"1820","DOI":"10.1007\/s12083-019-00793-5","article-title":"UAV-Assisted wireless relay networks for mobile offloading and trajectory optimization","volume":"12","author":"Feng","year":"2019","journal-title":"Peer-To-Peer Netw. Appl."},{"issue":"6","key":"10.1016\/j.phycom.2021.101397_b30","doi-asserted-by":"crossref","first-page":"3747","DOI":"10.1109\/TWC.2017.2688328","article-title":"Energy-efficient UAV communication with trajectory optimization","volume":"16","author":"Zeng","year":"2017","journal-title":"IEEE Trans. Wireless Commun."},{"issue":"3","key":"10.1016\/j.phycom.2021.101397_b31","doi-asserted-by":"crossref","first-page":"2049","DOI":"10.1109\/TVT.2017.2706308","article-title":"Mobile edge computing via a UAV-mounted cloudlet: Optimization of bit allocation and path planning","volume":"67","author":"Jeong","year":"2018","journal-title":"IEEE Trans. Veh. Technol."},{"issue":"5","key":"10.1016\/j.phycom.2021.101397_b32","doi-asserted-by":"crossref","first-page":"1054","DOI":"10.1109\/TNN.1998.712192","article-title":"Reinforcement learning: an introduction","volume":"9","author":"Sutton","year":"1998","journal-title":"IEEE Trans. Neural Netw."},{"year":"2018","author":"Chen","series-title":"Decentralized computation offloading for multi-user mobile edge computing: A deep reinforcement learning approach","key":"10.1016\/j.phycom.2021.101397_b33"},{"doi-asserted-by":"crossref","unstructured":"D. Pandey, P. Pandey, Approximate Q-Learning: An Introduction, in: 2010 Second International Conference on Machine Learning and Computing, 2010, pp. 317-320.","key":"10.1016\/j.phycom.2021.101397_b34","DOI":"10.1109\/ICMLC.2010.38"},{"year":"2013","author":"Mnih","series-title":"Playing atari with deep reinforcement learning","key":"10.1016\/j.phycom.2021.101397_b35"},{"issue":"7540","key":"10.1016\/j.phycom.2021.101397_b36","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1038\/nature14236","article-title":"Human-level control through deep reinforcement learning","volume":"518","author":"Mnih","year":"2015","journal-title":"Nature"},{"doi-asserted-by":"crossref","unstructured":"H. Sasaki, T. Horiuchi, S. Kato, A study on vision-based mobile robot learning by deep Q-network, in: Proceedings of the 56th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2017, 2017, pp. 799\u2013804.","key":"10.1016\/j.phycom.2021.101397_b37","DOI":"10.23919\/SICE.2017.8105597"},{"unstructured":"H. Van\u00a0Hasselt, Double Q-learning, in: Annual Conference on Advances in Neural Information Processing Systems (2010), 2010, pp. 2613\u20132621.","key":"10.1016\/j.phycom.2021.101397_b38"},{"year":"2015","author":"van Hasselt","series-title":"Deep reinforcement learning with double Q-learning","key":"10.1016\/j.phycom.2021.101397_b39"},{"year":"2015","author":"Abadi","series-title":"Tensorflow: Large-scale machine learning on heterogeneous distributed systems","key":"10.1016\/j.phycom.2021.101397_b40"},{"key":"10.1016\/j.phycom.2021.101397_b41","series-title":"Proceedings of the IEEE First International Conference on Neural Networks (San Diego, CA), Vol. III","first-page":"11","article-title":"Kolmogorov\u2019s mapping neural network existence theorem","author":"Nielsen","year":"1987"}],"container-title":["Physical Communication"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1874490721001348?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1874490721001348?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2023,4,16]],"date-time":"2023-04-16T06:34:28Z","timestamp":1681626868000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1874490721001348"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8]]},"references-count":41,"alternative-id":["S1874490721001348"],"URL":"https:\/\/doi.org\/10.1016\/j.phycom.2021.101397","relation":{},"ISSN":["1874-4907"],"issn-type":[{"type":"print","value":"1874-4907"}],"subject":[],"published":{"date-parts":[[2021,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A multi-user service migration scheme based on deep reinforcement learning and SDN in mobile edge computing","name":"articletitle","label":"Article Title"},{"value":"Physical Communication","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.phycom.2021.101397","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2021 Elsevier B.V. All rights reserved.","name":"copyright","label":"Copyright"}],"article-number":"101397"}}