{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T04:38:50Z","timestamp":1780547930533,"version":"3.54.1"},"reference-count":47,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2021,1,13]],"date-time":"2021-01-13T00:00:00Z","timestamp":1610496000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"National Key R8D Program of China","award":["2020YFB1006003"],"award-info":[{"award-number":["2020YFB1006003"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61902236"],"award-info":[{"award-number":["61902236"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Internet Technol."],"published-print":{"date-parts":[[2021,2,28]]},"abstract":"<jats:p>The emergence of mobile service composition meets the current needs for real-time eCommerce. However, the requirements for eCommerce, such as safety and timeliness, are becoming increasingly strict. Thus, the cloud-edge hybrid computing model has been introduced to accelerate information processing, especially in a mobile scenario. However, the mobile environment is characterized by limited resource storage and users who frequently move, and these characteristics strongly affect the reliability of service composition running in this environment. Consequently, applications are likely to fail if inappropriate services are invoked. To ensure that the composite service can operate normally, traditional dynamic reconfiguration methods tend to focus on cloud services scheduling. Unfortunately, most of these approaches cannot support timely responses to dynamic changes. In this article, the cloud-edge based dynamic reconfiguration to service workflow for mobile eCommerce environments is proposed. First, the service quality concept is extended. Specifically, the value and cost attributes of a service are considered. The value attribute is used to assess the stability of the service for some time to come, and the cost attribute is the cost of a service invocation. Second, a long short-term memory (LSTM) neural network is used to predict the stability of services, which is related to the calculation of the value attribute. Then, in view of the limited available equipment resources, a method for calculating the cost of calling a service is introduced. Third, candidate services are selected by considering both service stability and the cost of service invocation, thus yielding a dynamic reconfiguration scheme that is more suitable for the cloud-edge environment. Finally, a series of comparative experiments were carried out, and the experimental results prove that the method proposed in this article offers higher stability, less energy consumption, and more accurate service prediction.<\/jats:p>","DOI":"10.1145\/3391198","type":"journal-article","created":{"date-parts":[[2021,1,13]],"date-time":"2021-01-13T11:17:41Z","timestamp":1610536661000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":118,"title":["The Cloud-edge-based Dynamic Reconfiguration to Service Workflow for Mobile Ecommerce Environments"],"prefix":"10.1145","volume":"21","author":[{"given":"Honghao","family":"Gao","sequence":"first","affiliation":[{"name":"School of Computer Engineering and Science, Shanghai University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wanqiu","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Computer Engineering and Science, Shanghai University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yucong","family":"Duan","sequence":"additional","affiliation":[{"name":"College of Information Science and Technology, Hainan University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2021,1,13]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Proceedings of the APSCC. 107--114","author":"Li Y.","unstructured":"Y. Li , Y. Lu , Y. Yin , S. Deng , and J. Yin . 2010. Towards QoS-based dynamic reconfiguration of SOA-based applications . In Proceedings of the APSCC. 107--114 . Y. Li, Y. Lu, Y. Yin, S. Deng, and J. Yin. 2010. Towards QoS-based dynamic reconfiguration of SOA-based applications. In Proceedings of the APSCC. 107--114."},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/MCC.2016.92"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/MNET.2014.6963805"},{"key":"e_1_2_1_4_1","first-page":"547","article-title":"Applying probabilistic model checking to path planning in an intelligent transportation system using mobility trajectories and their statistical data","volume":"25","author":"Gao H.","year":"2019","unstructured":"H. Gao , W. Huang , and X. Yang . 2019 . Applying probabilistic model checking to path planning in an intelligent transportation system using mobility trajectories and their statistical data . Intell. Autom. Soft Comput. (Autosoft) 25 , 3 (2019), 547 -- 559 . H. Gao, W. Huang, and X. Yang. 2019. Applying probabilistic model checking to path planning in an intelligent transportation system using mobility trajectories and their statistical data. Intell. Autom. Soft Comput. (Autosoft) 25, 3 (2019), 547--559.","journal-title":"Intell. Autom. Soft Comput. (Autosoft)"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1080\/00207160.2013.782398"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jss.2017.05.125"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11036-012-0368-0"},{"key":"e_1_2_1_8_1","volume-title":"Proceedings of the CSPS. 1659--1666","author":"Yang Y.","unstructured":"Y. Yang , H. Zhao , and X. Gu . 2017. Improve energy consumption and packet scheduling for mobile edge computing . In Proceedings of the CSPS. 1659--1666 . Y. Yang, H. Zhao, and X. Gu. 2017. Improve energy consumption and packet scheduling for mobile edge computing. In Proceedings of the CSPS. 1659--1666."},{"key":"e_1_2_1_9_1","first-page":"5614","article-title":"Joint optimization for residual energy maximization in wireless powered mobile-edge computing systems","volume":"12","author":"Liu P.","year":"2018","unstructured":"P. Liu , G. Xu , K. Yang , K. Wang , and Y. Li . 2018 . Joint optimization for residual energy maximization in wireless powered mobile-edge computing systems . KSII Trans. Internet Inf. Syst. 12 , 12 (2018), 5614 -- 5633 . P. Liu, G. Xu, K. Yang, K. Wang, and Y. Li. 2018. Joint optimization for residual energy maximization in wireless powered mobile-edge computing systems. KSII Trans. Internet Inf. Syst. 12, 12 (2018), 5614--5633.","journal-title":"KSII Trans. Internet Inf. Syst."},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.2010.5560598"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1039\/b820555h"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNET.2015.2487344"},{"key":"e_1_2_1_13_1","volume-title":"Proceedings of the IJCNN. 1--8.","author":"Gary W.","unstructured":"W. Gary , A. Palade , and S. Clarke . 2018. Forecasting QoS attributes using LSTM networks . In Proceedings of the IJCNN. 1--8. W. Gary, A. Palade, and S. Clarke. 2018. Forecasting QoS attributes using LSTM networks. In Proceedings of the IJCNN. 1--8."},{"key":"e_1_2_1_14_1","volume-title":"Proceedings of the Network TMA. 1--6.","author":"Diego M.","unstructured":"M. Diego , M. Panza , and J. Bustos-Jim\u00e9nez . 2018. I'm only unhappy when it rains: Forecasting mobile QoS with weather conditions . In Proceedings of the Network TMA. 1--6. M. Diego, M. Panza, and J. Bustos-Jim\u00e9nez. 2018. I'm only unhappy when it rains: Forecasting mobile QoS with weather conditions. In Proceedings of the Network TMA. 1--6."},{"key":"e_1_2_1_15_1","doi-asserted-by":"crossref","unstructured":"D. Miorandi S. Sicari F. D. Pellegrini and I. Chlamtac. 2009. Internet of things: Vision applications and research challenges. Ad hoc Netw. 10 7 (2009) 1497--1516.  D. Miorandi S. Sicari F. D. Pellegrini and I. Chlamtac. 2009. Internet of things: Vision applications and research challenges. Ad hoc Netw. 10 7 (2009) 1497--1516.","DOI":"10.1016\/j.adhoc.2012.02.016"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/TASE.2015.2438020"},{"key":"e_1_2_1_17_1","first-page":"755","article-title":"Research on cost-driven services composition in an uncertain environment","volume":"20","author":"Gao H.","year":"2019","unstructured":"H. Gao , W. Huang , Y. Duan , and Q. Zou . 2019 . Research on cost-driven services composition in an uncertain environment . J. Internet Technol. 20 , 3 (2019), 755 -- 769 . H. Gao, W. Huang, Y. Duan, and Q. Zou. 2019. Research on cost-driven services composition in an uncertain environment. J. Internet Technol. 20, 3 (2019), 755--769.","journal-title":"J. Internet Technol."},{"key":"e_1_2_1_18_1","volume-title":"Proceedings of the ICSOC. 287--294","author":"Labbaci H.","unstructured":"H. Labbaci , B. Medjahed , and Y. Aklouf . 2017. A deep learning approach for long term QoS-compliant service composition . In Proceedings of the ICSOC. 287--294 . H. Labbaci, B. Medjahed, and Y. Aklouf. 2017. A deep learning approach for long term QoS-compliant service composition. In Proceedings of the ICSOC. 287--294."},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2015.2446443"},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.adhoc.2012.02.016"},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/TASE.2015.2438020"},{"key":"e_1_2_1_22_1","volume-title":"Proceedings of the NIPS. 802--810","author":"Shi X.","unstructured":"X. Shi , Z. Chen , H. Wang , D. Y. Yeung , W. K. Wong , and W. C. Woo . 2015. Convolutional LSTM network: A machine learning approach for precipitation nowcasting . In Proceedings of the NIPS. 802--810 . X. Shi, Z. Chen, H. Wang, D. Y. Yeung, W. K. Wong, and W. C. Woo. 2015. Convolutional LSTM network: A machine learning approach for precipitation nowcasting. In Proceedings of the NIPS. 802--810."},{"key":"e_1_2_1_23_1","volume-title":"Proceedings of the ICML.","author":"Laptev N.","unstructured":"N. Laptev , J. Yosinski , E. L. Li , and S. Smyl . 2017. Time-series extreme event forecasting with neural networks at Uber . In Proceedings of the ICML. N. Laptev, J. Yosinski, E. L. Li, and S. Smyl. 2017. Time-series extreme event forecasting with neural networks at Uber. In Proceedings of the ICML."},{"key":"e_1_2_1_24_1","volume-title":"Proceedings of the SSST.","author":"Cho K.","unstructured":"K. Cho , B. V. Merri\u00ebnboer , D. Bahdanau , and Y. Bengio . 2014. On the properties of neural machine translation: Encoder-decoder approaches . In Proceedings of the SSST. K. Cho, B. V. Merri\u00ebnboer, D. Bahdanau, and Y. Bengio. 2014. On the properties of neural machine translation: Encoder-decoder approaches. In Proceedings of the SSST."},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2019.2956827"},{"key":"e_1_2_1_26_1","unstructured":"C. Olah. 2015. Understanding LSTM networks. Retrieved from http:\/\/colah.github.io\/posts\/2015-08-Understanding-LSTMs\/.  C. Olah. 2015. Understanding LSTM networks. Retrieved from http:\/\/colah.github.io\/posts\/2015-08-Understanding-LSTMs\/."},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1002\/wcm.72"},{"key":"e_1_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/JRPROC.1946.234568"},{"key":"e_1_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.3724\/SP.J.1001.2012.04084"},{"key":"e_1_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSC.2012.34"},{"key":"e_1_2_1_31_1","volume-title":"Proceedings of the PESOS. 64--70","author":"Bice C.","unstructured":"C. Bice , M. Di . Penta, and G. Canfora . 2010. An empirical comparison of methods to support QoS-aware service selection . In Proceedings of the PESOS. 64--70 . C. Bice, M. Di. Penta, and G. Canfora. 2010. An empirical comparison of methods to support QoS-aware service selection. In Proceedings of the PESOS. 64--70."},{"key":"e_1_2_1_32_1","volume-title":"Proceedings of the WINE.","author":"Gao H.","unstructured":"H. Gao , Y. Duan , L. Shao , and X. Sun . 2019. Transformation-based processing of typed resources for multimedia sources in the IoT environment . In Proceedings of the WINE. H. Gao, Y. Duan, L. Shao, and X. Sun. 2019. Transformation-based processing of typed resources for multimedia sources in the IoT environment. In Proceedings of the WINE."},{"key":"e_1_2_1_33_1","volume-title":"Proceedings of the ICEIEC. 233--236","author":"Fan D.","unstructured":"D. Fan , D. Wang , L. Pan , and F. Xiao . 2018. Reconfiguration of adaptors based on trace compliance in cloud service composition . In Proceedings of the ICEIEC. 233--236 . D. Fan, D. Wang, L. Pan, and F. Xiao. 2018. Reconfiguration of adaptors based on trace compliance in cloud service composition. In Proceedings of the ICEIEC. 233--236."},{"key":"e_1_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1002\/spe.2457"},{"key":"e_1_2_1_35_1","volume-title":"Proceedings of the Monterey Workshop. 87--98","author":"Xu W.","unstructured":"W. Xu , X. Zhong , Y. Zhao , Z. Zhou , L. Zhang , and D. Pham . 2016. Manufacturing service reconfiguration optimization using hybrid bees algorithm in cloud manufacturing . In Proceedings of the Monterey Workshop. 87--98 . W. Xu, X. Zhong, Y. Zhao, Z. Zhou, L. Zhang, and D. Pham. 2016. Manufacturing service reconfiguration optimization using hybrid bees algorithm in cloud manufacturing. In Proceedings of the Monterey Workshop. 87--98."},{"key":"e_1_2_1_36_1","volume-title":"Proceedings of the FAS*W. 54--59","author":"Kim S.","unstructured":"S. Kim , Y. Han , and S. Park . 2016. An energy-aware service function chaining and reconfiguration algorithm in NFV . In Proceedings of the FAS*W. 54--59 . S. Kim, Y. Han, and S. Park. 2016. An energy-aware service function chaining and reconfiguration algorithm in NFV. In Proceedings of the FAS*W. 54--59."},{"key":"e_1_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jss.2018.05.069"},{"key":"e_1_2_1_38_1","unstructured":"B. Fouzia S. Sadouki and A. Tari. 2019. A bio-inspired algorithm for dynamic reconfiguration with end-to-end constraints in web services composition. Serv.-orient. Comput. Applic. 1--10.  B. Fouzia S. Sadouki and A. Tari. 2019. A bio-inspired algorithm for dynamic reconfiguration with end-to-end constraints in web services composition. Serv.-orient. Comput. Applic. 1--10."},{"key":"e_1_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1142\/S0218194016400040"},{"key":"e_1_2_1_40_1","doi-asserted-by":"crossref","unstructured":"Y. Yin L. Chen Y. Xu J. Wan H. Zhang and Z. Mai. 2019. QoS prediction for service recommendation with deep feature learning in edge computing environment. Mob. Netw. Applic\u2014. 25 (2019) 391--401.  Y. Yin L. Chen Y. Xu J. Wan H. Zhang and Z. Mai. 2019. QoS prediction for service recommendation with deep feature learning in edge computing environment. Mob. Netw. Applic\u2014. 25 (2019) 391--401.","DOI":"10.1007\/s11036-019-01241-7"},{"key":"e_1_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1109\/MIC.2019.2893872"},{"key":"e_1_2_1_42_1","volume-title":"Proceedings of the MOBIHOC. 291--300","author":"Hou I.","unstructured":"I. Hou , T. Zhao , S. Wang , and K. Chan . 2016. Asymptotically optimal algorithm for online reconfiguration of edge-clouds . In Proceedings of the MOBIHOC. 291--300 . I. Hou, T. Zhao, S. Wang, and K. Chan. 2016. Asymptotically optimal algorithm for online reconfiguration of edge-clouds. In Proceedings of the MOBIHOC. 291--300."},{"key":"e_1_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"e_1_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2016.2582924"},{"key":"e_1_2_1_45_1","volume-title":"Proceedings of the ICANN. 850--855","author":"Gers F. A.","unstructured":"F. A. Gers , J. Schmidhuber , and F. Cummins . 1999. Learning to forget: Continual prediction with LSTM . In Proceedings of the ICANN. 850--855 . F. A. Gers, J. Schmidhuber, and F. Cummins. 1999. Learning to forget: Continual prediction with LSTM. In Proceedings of the ICANN. 850--855."},{"key":"e_1_2_1_46_1","volume-title":"Proceedings of the ICLR.","author":"Kingma D. P.","unstructured":"D. P. Kingma and J. Ba . 2014. Adam: A method for stochastic optimization . In Proceedings of the ICLR. D. P. Kingma and J. Ba. 2014. Adam: A method for stochastic optimization. In Proceedings of the ICLR."},{"key":"e_1_2_1_47_1","article-title":"Skyline service selection approach based on QoS","volume":"13","author":"Guo Y.","year":"2017","unstructured":"Y. Guo , S. Wang , K. Wong , and M. Kim . 2017 . Skyline service selection approach based on QoS . Int. J. WebGrid Serv. 13 , 4 (2017). Y. Guo, S. Wang, K. Wong, and M. Kim. 2017. Skyline service selection approach based on QoS. Int. J. WebGrid Serv. 13, 4 (2017).","journal-title":"Int. J. WebGrid Serv."}],"container-title":["ACM Transactions on Internet Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3391198","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3391198","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T22:38:37Z","timestamp":1750199917000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3391198"}},"subtitle":["A QoS Prediction Perspective"],"short-title":[],"issued":{"date-parts":[[2021,1,13]]},"references-count":47,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,2,28]]}},"alternative-id":["10.1145\/3391198"],"URL":"https:\/\/doi.org\/10.1145\/3391198","relation":{},"ISSN":["1533-5399","1557-6051"],"issn-type":[{"value":"1533-5399","type":"print"},{"value":"1557-6051","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,13]]},"assertion":[{"value":"2019-11-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2020-03-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-01-13","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}