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Browsing by Author "Li, Fan"

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Now showing 1 - 15 of 15
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    A-DDPG: Attention Mechanism-based Deep Reinforcement Learning for NFV
    (IEEE, 2021)
    He, Nan
    ;
    Yang, Song
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    Li, Fan
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    Trajanovski, Stojan
    ;
    Kuipers, Fernando A.
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    Fu, Xiaoming
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    Adaptive and Efficient Qubit Allocation Using Reinforcement Learning in Quantum Networks
    (2022)
    Gao, Yanan
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    Yang, Song
    ;
    Li, Fan
    ;
    Fu, Xiaoming
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    Caching-Enabled Computation Offloading in Multi-Region MEC Network via Deep Reinforcement Learning
    (2022)
    Yang, Song
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    Liu, Jintian
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    Zhang, Fei
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    Li, Fan
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    Chen, Xu
    ;
    Fu, Xiaoming
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    Cloudlet Placement and Task Allocation in Mobile Edge Computing
    (2019)
    Yang, Song
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    Li, Fan
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    Shen, Meng
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    Chen, Xu
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    Fu, Xiaoming
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    Wang, Yu
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    Delay-Aware Virtual Network Function Placement and Routing in Edge Clouds
    (2021)
    Yang, Song
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    Li, Fan
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    Trajanovski, Stojan
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    Chen, Xu
    ;
    Wang, Yu
    ;
    Fu, Xiaoming
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    Delay-Sensitive and Availability-Aware Virtual Network Function Scheduling for NFV
    (2019)
    Yang, Song
    ;
    Li, Fan
    ;
    Yahyapour, Ramin  
    ;
    Fu, Xiaoming
    Network Function Virtualization (NFV) has been emerging as an appealing solution that transforms from dedicated hardware implementations to software instances running in a virtualized environment. In NFV, the requested service is implemented by a sequence of Virtual Network Functions (VNF) that can run on generic servers by leveraging the virtualization technology. These VNFs are pitched with a predefined order, and it is also known as the Service Function Chaining (SFC). Considering that the delay and resiliency are two important Service Level Agreements (SLA) in a NFV service, in this paper, we first investigate how to quantitatively model the traversing delay of a flow in both totally ordered and partially ordered SFCs. Subsequently, we study how to calculate the VNF placement availability mathematically for both unprotected and protected SFCs. After that, we study the delay-sensitive Virtual Network Function (VNF) placement and routing problem with and without resiliency concerns. We prove that this problem is NP-hard under two cases. We subsequently propose an exact Integer Nonlinear Programming formulation and an efficient heuristic for this problem in each case. Finally, we evaluate the proposed algorithms in terms of acceptance ratio, average number of used nodes and total running time via extensive simulations.
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    Gamora: Learning-Based Buffer-Aware Preloading for Adaptive Short Video Streaming
    (2024)
    Hou, Biao
    ;
    Yang, Song
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    Li, Fan
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    Zhu, Liehuang
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    Jiao, Lei
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    Chen, Xu
    ;
    Fu, Xiaoming
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    Leveraging Deep Reinforcement Learning With Attention Mechanism for Virtual Network Function Placement and Routing
    (2023)
    He, Nan
    ;
    Yang, Song
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    Li, Fan
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    Trajanovski, Stojan
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    Zhu, Liehuang
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    Wang, Yu
    ;
    Fu, Xiaoming
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    Making Serverless Not So Cold in Edge Clouds: A Cost-Effective Online Approach
    (2024)
    Xiao, Ke
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    Yang, Song
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    Li, Fan
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    Zhu, Liehuang
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    Chen, Xu
    ;
    Fu, Xiaoming
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    Online Control of Service Function Chainings Across Geo-Distributed Datacenters
    (2023)
    Yang, Song
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    Li, Fan
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    Zhou, Zhi
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    Chen, Xu
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    Wang, Yu
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    Fu, Xiaoming
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    Recent Advances of Resource Allocation in Network Function Virtualization
    (2021)
    Yang, Song
    ;
    Li, Fan
    ;
    Trajanovski, Stojan
    ;
    Yahyapour, Ramin  
    ;
    Fu, Xiaoming
    Network Function Virtualization (NFV) has been emerging as an appealing solution that transforms complex network functions from dedicated hardware implementations to software instances running in a virtualized environment. Due to the numerous advantages such as flexibility, efficiency, scalability, short deployment cycles, and service upgrade, NFV has been widely recognized as the next-generation network service provisioning paradigm. In NFV, the requested service is implemented by a sequence of Virtual Network Functions (VNF) that can run on generic servers by leveraging the virtualization technology. These VNFs are pitched with a predefined order through which data flows traverse, and it is also known as the Service Function Chaining (SFC). In this article, we provide an overview of recent advances of resource allocation in NFV. We generalize and analyze four representative resource allocation problems, namely, (1) the VNF Placement and Traffic Routing problem, (2) VNF Placement problem, (3) Traffic Routing problem in NFV, and (4) the VNF Redeployment and Consolidation problem. After that, we study the delay calculation models and VNF protection (availability) models in NFV resource allocation, which are two important Quality of Service (QoS) parameters. Subsequently, we classify and summarize the representative work for solving the generalized problems by considering various QoS parameters (e.g., cost, delay, reliability, and energy) and different scenarios (e.g., edge cloud, online provisioning, and distributed provisioning). Finally, we conclude our article with a short discussion on the state-of-the-art and emerging topics in the related fields, and highlight areas where we expect high potential for future research.
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    Risk factors involved in the formation of multiple intracranial aneurysms
    (2020)
    Xin, Wen-qiang
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    Sun, Peng-ju
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    Li, Fan
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    Cheng, Ming-xun
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    Yang, Shi-xue
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    Cui, Bao-long
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    Wang, Zeng-guang
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    Yang, Xin-yu
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    Survivable Task Allocation in Cloud Radio Access Networks With Mobile-Edge Computing
    (2021)
    Yang, Song
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    He, Nan
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    Li, Fan
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    Trajanovski, Stojan
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    Chen, Xu
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    Wang, Yu
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    Fu, Xiaoming
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    Traffic routing in stochastic network function virtualization networks
    (2020)
    Yang, Song
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    Li, Fan
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    Trajanovski, Stojan
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    Fu, Xiaoming
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    Video Content Placement At the Network Edge: Centralized and Distributed Algorithms
    (2022)
    Gao, Yanan
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    Yang, Song
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    Li, Fan
    ;
    Trajanovski, Stojan
    ;
    Zhou, Pan
    ;
    Hui, Pan
    ;
    Fu, Xiaoming

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