Integrating recurrent neural networks and reinforcement learning for dynamic service composition

K. Sangsanit, W. Kurutach, S. Phoomvuthisarn, REST web service composition: A survey of automation and techniques, in: 2018 International Conference on Information Networking, ICOIN, 2018, pp. 116–121. Yan, 2010, Repair vs. recomposition for broken service compositions, 152 Ardagna, 2007, Adaptive service composition in flexible processes, IEEE Trans. Softw. Eng., 33, 369, 10.1109/TSE.2007.1011 Li, 2018, Qos-aware service composition in cloud manufacturing: a Gale-Shapley algorithm-based approach, IEEE Trans. Syst. Man Cybern. Syst., 1 Yan, 2010, Self-adaptive service composition through graphplan repair, 624 Yan, 2010, Repairing service compositions in a changing world, 17 Alrifai, 2010, Selecting skyline services for qos-based web service composition, 11 V.A. Permadi, B.J. Santoso, Efficient skyline-based web service composition with QoS-awareness and budget constraint, in: 2018 International Conference on Information and Communications Technology, ICOIACT, 2018, pp. 855–860. Hammas, 2015, Adaptive web service composition insuring global QoS optimization, 1 Gavvala, 2019, Qos-aware cloud service composition using eagle strategy, Future Gener. Comput. Syst., 90, 273, 10.1016/j.future.2018.07.062 Wang, 2010, Adaptive service composition based on reinforcement learning, 92 H. Wang, G. Huang, Q. Yu, Automatic hierarchical reinforcement learning for efficient large-scale service composition, in: 2016 IEEE International Conference on Web Services, ICWS, 2016, pp. 57–64. Wang, 2014, Integrating on-policy reinforcement learning with multi-agent techniques for adaptive service composition, 154 y. lei, P.S. Yu, Multi-agent reinforcement learning for service composition, in: 2016 IEEE International Conference on Services Computing, SCC, 2016, pp. 790–793. Kendrick, 2019, An efficient multi-cloud service composition using a distributed multiagent-based, memory-driven approach, IEEE Trans. Sustain. Comput., 1, 10.1109/TSUSC.2018.2881416 Li, 2019, A trust-based agent learning model for service composition in mobile cloud computing environments, IEEE Access, 7, 34207, 10.1109/ACCESS.2019.2904081 Billsus, 1998, Learning collaborative information filters, 46 J.L. Herlocker, J.A. Konstan, A. Borchers, J. Riedl, An algorithmic framework for performing collaborative filtering, in: SIGIR ’99: Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval, August 15–19, 1999, Berkeley, CA, USA, 1999, pp. 230–237. Kim, 2010, Collaborative filtering based on collaborative tagging for enhancing the quality of recommendation, Electron. Commer. Res. Appl., 9, 73, 10.1016/j.elerap.2009.08.004 K. Chen, H. Mao, X. Shi, Y. Xu, A. Liu, Trust-aware and location-based collaborative filtering for web service QoS prediction, in: 2017 IEEE 41st Annual Computer Software and Applications Conference, COMPSAC, vol. 2, 2017, pp. 143–148. C. Wu, W. Qiu, X. Wang, Z. Zheng, X. Yang, Time-aware and sparsity-tolerant qos prediction based on collaborative filtering, in: 2016 IEEE International Conference on Web Services, ICWS, 2016, pp. 637–640. Zou, 2018, Qos-aware web service recommendation with reinforced collaborative filtering, 430 Calheiros, 2015, Workload prediction using ARIMA model and its impact on cloud applications x2019; QoS, IEEE Trans. Cloud Comput., 3, 449, 10.1109/TCC.2014.2350475 Sutskever, 2014, Sequence to sequence learning with neural networks, 3104 Graves, 2013, Speech recognition with deep recurrent neural networks, 6645 Cirstea, 2018, Correlated time series forecasting using multi-task deep neural networks, 1527 L. Yunpeng, H. Di, B. Junpeng, Q. Yong, Multi-step ahead time series forecasting for different data patterns based on LSTM recurrent neural network, in: 2017 14th Web Information Systems and Applications Conference, WISA, 2017, pp. 305–310. Kaelbling, 1991, A situated-automata approach to the design of embedded agents, ACM SIGART Bull., 2, 85, 10.1145/122344.122361 Goldberg, 1988, Genetic algorithms and machine learning, Mach. Learn., 3, 95, 10.1023/A:1022602019183 Crites, 1998, Elevator group control using multiple reinforcement learning agents, Mach. Learn., 33, 235, 10.1023/A:1007518724497 Hamilton, 1994 Contreras, 2003, ARIMA Models to predict next-day electricity prices, IEEE Trans. Power Syst., 18, 1014, 10.1109/TPWRS.2002.804943 LeCun, 2015, Deep learning, Nature, 521, 436, 10.1038/nature14539 Connor, 1994, Recurrent neural networks and robust time series prediction, IEEE Trans. Neural Netw., 5, 240, 10.1109/72.279188 Karpathy, 2015 Goller, 1996, Learning task-dependent distributed representations by backpropagation through structure, 347 Hochreiter, 1997, Long short-term memory, Neural Comput., 9, 1735, 10.1162/neco.1997.9.8.1735 Zhu, 2017, Robust web service recommendation via quantile matrix factorization, 1 Abadi, 2016