Energy-aware service composition in multi-Cloud
Tài liệu tham khảo
Palade, 2018, Stigmergy-based QoS optimisation for flexible service composition in mobile communities, 27
Asghari, 2018, Service composition approaches in IoT: a systematic review, J. Netw. Comput. Appl., 120, 61, 10.1016/j.jnca.2018.07.013
Hao, 2021, Energy-aware offloading based on priority in mobile cloud computing, Sustainable Computing: Informatics and Systems, 31
Hao, 2022, Interval grey number of energy consumption helps task offloading in the mobile environment, ICT Express, 10.1016/j.icte.2022.03.005
Huf, 2019, Composition of heterogeneous web services: a systematic review, J. Netw. Comput. Appl., 143, 89, 10.1016/j.jnca.2019.06.008
Ridhawi, 2015, Decentralized plan-free semantic-based service composition in mobile networks, IEEE Trans. Serv. Comput., 8, 17, 10.1109/TSC.2013.2297114
Zhang, 2016, A framework for truthful online auctions in cloud computing with heterogeneous user demands, IEEE Trans. Comput., 65, 805, 10.1109/TC.2015.2435784
Nguyen, 2022, 6G internet of things: a comprehensive survey, IEEE Internet Things J., 9, 359, 10.1109/JIOT.2021.3103320
Gu, 2010, Service data correlation modeling and its application in data-driven service composition, IEEE Trans. Serv. Comput., 3, 279, 10.1109/TSC.2010.22
Khanouche, 2020, Flexible QoS-aware services composition for service computing environments, Comput. Networks, 166, 106982, 10.1016/j.comnet.2019.106982
J. Lu, Y. Hao, L. Wang, M. Zheng, “Towards efficient service composition in multi-cloud environment,” Proc. - 2015 Int. Conf. Comput. Sci. Comput. Intell. CSCI 2015, pp. 65–70, 2016, 10.1109/CSCI.2015.69.
Pu, 2020, An online mechanism for resource allocation in networks, IEEE Trans. Control Netw. Syst., 7, 1140, 10.1109/TCNS.2020.2964142
Ma, 2013, QoS-driven service composition with reconfigurable services, IEEE Trans. Serv. Comput., 6, 20, 10.1109/TSC.2011.21
Liu, 2019, Large-scale and adaptive service composition based on deep reinforcement learning, J. Vis. Commun. Image Represent., 65, 10.1016/j.jvcir.2019.102687
Cai, 2018, Model-driven development patterns for mobile services in cloud of things, IEEE Trans. Cloud Comput., 6, 771, 10.1109/TCC.2016.2526007
Dibaei, 2022, Investigating the prospect of leveraging blockchain and machine learning to secure vehicular networks: a survey, IEEE Trans. Intell. Transp. Syst., 23, 683, 10.1109/TITS.2020.3019101
Yang, 2022, BrainIoT: brain-like productive services provisioning with federated learning in industrial IoT, IEEE Internet Things J., 9, 2014, 10.1109/JIOT.2021.3089334
Sun, 2020, Energy-efficient provisioning for service function chains to support delay-sensitive applications in network function virtualization, IEEE Internet Things J., 7, 6116, 10.1109/JIOT.2020.2970995
Kurdi, 2015, A combinatorial optimization algorithm for multiple cloud service composition, Comput. Elec. Eng., 42, 107, 10.1016/j.compeleceng.2014.11.002
Entezari-Maleki, 2020, Modeling and evaluation of service composition in commercial multiclouds using timed colored petri nets, IEEE Trans. Syst. Man Cybern. Syst., 50, 947, 10.1109/TSMC.2017.2768586
Li, 2019, Energy efficient computation offloading for nonorthogonal multiple access assisted mobile edge computing with energy harvesting devices, Comput. Networks, 164, 10.1016/j.comnet.2019.106890
Institute of Electrical and Electronics Engineers, 2017 IEEE 86th Vehicular Technology Conference (VTC Fall) : proceedings : Toronto, Canada, 24-27 September 2017., 2017.