@article {1773,
	title = {Leveraging Deep Reinforcement Learning with Attention Mechanism for Virtual Network Function Placement and Routing},
	journal = {IEEE  Transactions on Parallel and Distributed Systems},
	volume = {34},
	year = {2023},
	month = {04/2023},
	pages = {1186-1201},
	author = {Nan He and Song Yang and Fan Li and Stojan Trajanovski and Liehuang Zhu and Yu Wang and Xiaoming Fu}
}
@article {1708,
	title = {Survivable Task Allocation in Cloud Radio Access Networks with Mobile Edge Computing},
	journal = {IEEE Internet of Things Journal},
	volume = {8},
	year = {2021},
	month = {01/2021},
	pages = {1095-1108},
	author = {Song Yang and Nan He and Fan Li and Stojan Trajanovski and Xu Chen and Yu Wang and Xiaoming Fu}
}
@article {1671,
	title = {Cloudlet Placement and Task Allocation in Mobile Edge Computing},
	journal = {IEEE Internet of Things Journal},
	volume = {6},
	year = {2019},
	month = {06/2019},
	pages = {5853-5863},
	abstract = {<p>Mobile edge computing (MEC) offers a way to shorten the cloud servicing delay by building the small-scale cloud infrastructures, such as cloudlets at the network edge, which are in close proximity to end users. On one hand, it is energy consuming and costly to place each cloudlet on each access point (AP) to process the requested tasks. On the other hand, the service provider should provide delay-guaranteed service to end users, otherwise they may get revenue loss. In this paper, we first model how to calculate the task completion delay in MEC and mathematically analyze the energy consumption of different equipments in MEC. Subsequently, we study how to place cloudlets on the network and allocate each requested task to cloudlets and public cloud with the minimum total energy consumption without violating each task{\textquoteright}s delay requirement. We prove that this problem is NP-hard and propose a Benders decomposition-based algorithm to solve it. We also present a software-defined network (SDN)-based framework to deploy the proposed algorithm. Extensive simulations reveal that the proposed algorithm can achieve an (close-to-)optimal performance in terms of energy consumption and acceptance ratio compared with two benchmark heuristics.</p>
},
	doi = {10.1109/JIOT.2019.2907605},
	author = {Song Yang and Fan Li and Meng Shen and Xu Chen and Xiaoming Fu and Yu Wang}
}
