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【论文汇总】Papers on NFV-RA

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Lastest update: JUN. 12, 2021.
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This is a paper list about Resource Allocation in Network Functions Virtualization (NFV) and Software-Defined Networking (SDN) including

  • Comprehensive Surveys
  • VNE: Virtual Network Embedding Algorithms
  • VNFC: Virtual Network Functions Chaining Algorithms
  • VNFP: Virtual Network Functions Placement Algorithms
  • VNFF: Virtual Network Functions Migration Algorithms
  • VNFS: Virtual Network Functions Scheduling Algorithms
  • Multi-domain: also known as cross-domain, multi-region or other resemble name.

Particularly, we mainly collect papers from high-quality journals and conferences, and classify them according to method categories.

Favorably receive that submit relevant papers to this repository in the appropriate format.

Search by Keywords

You can search the relevant papers by following keywords:

  • Direction: VNE, VNFC, VNFP, VNFF, VNFS, Multi-domain
  • Publication: JSAC, TON, INFOCOM, CN, ...
  • PUB-rank: CCF-A, CCF-B, JCR-Q1, ...
  • Awareness: Latency, Reliability, Congestion, Privacy
  • RL-ALGO: DQN, DDPG, A3C, ...
  • NN-type: CNN, RNN, GNN, ...

Content

Survey papers

  1. Recent Advances of Resource Allocation in Network Function Virtualization

    • Publication: TPDS 2021 (CCF-A)
    • Authors: Song Yang, Fan Li, Stojan Trajanovski, Ramin Yahyapour, Xiaoming Fu
    • Link: IEEE Xplore

  2. SDN/NFV-Empowered Future IoV With Enhanced Communication, Computing, and Caching

    • Publication: Proc. IEEE 2020 (CCF-A)
    • Authors: Weihua Zhuang; Qiang Ye; Feng Lyu; Nan Cheng; Ju Ren
    • Link: IEEE Xplore

  3. Survey of Performance Acceleration Techniques for Network Function Virtualization

    • Publication: Proc. IEEE 2019 (CCF-A)
    • Authors: Leonardo Linguaglossa; Stanislav Lange; Salvatore Pontarelli; Gábor Rétvári; Dario Rossi; Thomas Zinner; Roberto Bifulco; Michael; Jarschel; Giuseppe Bianchi
    • Link: IEEE Xplore

  4. Will Serverless Computing Revolutionize NFV?

    • Publication: Proc. IEEE 2019 (CCF-A)
    • Authors: Paarijaat Aditya; Istemi Ekin Akkus; Andre Beck; Ruichuan Chen; Volker Hilt; Ivica Rimac; Klaus Satzke; Manuel Stein
    • Link: IEEE Xplore

  5. A Survey on the Placement of Virtual Resources and Virtual Network Functions

    • Publication: IEEE Communications Surveys & Tutorials 2019 (JCR-Q1)
    • Authors: Abdelquoddouss Laghrissi and Tarik Taleb
    • Link: paper

  6. Resource Allocation in NFV: A Comprehensive Survey

    • Publication: TNSM 2019 (CCF-C)
    • Authors: Juliver Gil Herrera, Juan Felipe Botero
    • Link: IEEE Xplore

  7. A comprehensive survey of network function virtualization

    • Publication: CN 2018 (CCF-B)
    • Authors: Bo Yi, Xingwei Wang, Keqin Li, Sajal k. Das , Min Huang
    • Link: ScienceDirect

Mathematical-based

Heuristic-based

Basic Heuristic

  1. Energy and Cost Efficient Resource Allocation for Blockchain-Enabled NFV

    • Publication: TSC 2021 (CCF-B)
    • Authors: Shiva Kazemi Taskou, Mehdi Rasti, Pedro H. J. Nardelli
    • Keyworks: VNFP, Blockchain-Enabled, HuRA (Hungarian-based Resource Allocation), HuRA (Hungarian-based Resource Allocation)
    • Objective: Minimize the energy consumption and utilized resource cost simultaneously
    • Link: paper

  2. Towards Latency Optimization in Hybrid Service Function Chain Composition and Embedding

    • Publication: INFOCOM 2020 (CCF-A)
    • Authors: Panpan Jin; Xincai Fei; Qixia Zhang; Fangming Liu; Bo Li
    • Keyworks: VNFC & VNFP, HSFCE (Hybrid SFC composition and Embedding), Latency-aware, Betweenness Centrality
    • Objective: Minimize the latency for the constructed hybrid SFP
    • Link: IEEE Xplore

  3. Latency-aware VNF Chain Deployment with Efficient Resource Reuse at Network Edge

    • Publication: INFOCOM 2020 (CCF-A)
    • Authors: Panpan Jin; Xincai Fei; Qixia Zhang; Fangming Liu; Bo Li
    • Keyworks: VNFP, MILP (Mixed Integer Iinear Programming), Latency-aware, CDFSA (constrained depth-first search algorithm)
    • Objective: Minimize the resource consumption of both servers and links with latency guarantees
    • Link: paper

  4. An Online Algorithm for VNF Service Chain Scaling in Datacenters

    • Publication: TON 2020 (CCF-A)
    • Authors: Ziyue Luo, Chuan Wu
    • Keyworks: VNFP, ILP (Integer Linear Program), Regularization, Rounding
    • Objective: Minimize the operating cost and deployment cost
    • Link: paper

  5. Reliability-Aware Virtualized Network Function Services Provisioning in Mobile Edge Computing

    • Publication: TON 2020 (CCF-A)
    • Authors: Meitian Huang, Weifa Liang, Xiaojun Shen, Yu Ma, Haibin Kan
    • Keyworks: VNFP, Reliability-aware, approximation algorithms, DP (dynamic programming), MEC (mobile edge computing)
    • Objective: Maximize the network throughput
    • Link: IEEE Xplore

  6. Congestion-Aware and Energy-Aware Virtual Network Embedding

    • Publication: TON 2020 (CCF-A)
    • Authors: Minh Pham, Doan B. Hoang, Zenon Chaczko
    • Keyworks: VNE, relaxed LP (linear Program), Congestion-aware, Energy-aware, SDN (Software-Defined Networks), SR (Segment Routing)
    • Objective: Multiple-objective is to save cost, save energy and avoid network congestion simultaneously
    • Link: IEEE Xplore

  7. Sova: A Software-Defined Autonomic Framework for Virtual Network Allocations

    • Publication: TPDS 2020 (CCF-A)
    • Authors: Zhiyong Ye, Yang Wang, Shuibing He, Chengzhong Xu, Xian-He Sun
    • Keyworks: VNFP, VNFM, SDN
    • Objective: Optimize the network allocation between different services by coordinating virtual dynamic SR-IOV and virtual machine live migration in autonomic way
    • Link: IEEE Xplore

  8. Optimal Virtual Network Function Deployment for 5G Network Slicing in a Hybrid Cloud Infrastructure

    • Publication: TWC 2020 (CCF-B)
    • Authors: Antonio De Domenico, Ya-Feng Liu, Wei Yu
    • Keyworks: VNFP, ILP (Integer Linear Programming), Network Slicing
    • Objective: Lead to high resource utilization efficiency and large gains in terms of the number of supported VNF chains
    • Link: IEEE Xplore

  9. Cost-Efficient VNF Placement and Scheduling in Public Cloud Networks

    • Publication: TCOM 2020 (CCF-B)
    • Authors: Tao Gao, Xin Li, Yu Wu , Weixia Zou, Shanguo Huang, Massimo Tornatore, Biswanath Mukherjee
    • Keyworks: VNFP, VNFS, Cost Efficiency, Public Cloud
    • Objective: /
    • Link: IEEE Xplore

  10. Virtual Network Embedding With Guaranteed Connectivity Under Multiple Substrate Link Failures

    • Publication: TCOM 2020 (CCF-B)
    • Authors: Zhiyong Ye, Yang Wang, Shuibing He, Chengzhong Xu, Xian-He Sun
    • Keyworks: VNE, Connectivity, Fault Tolerance, Redundancy
    • Objective: /
    • Link: IEEE Xplore

  11. Reliability Aware Service Placement Using a Viterbi-Based Algorithm

    • Publication: TNSM 2020 (CCF-C)
    • Authors: Mohammad Karimzadeh-Farshbafan, Vahid Shah-Mansouri, Dusit Niyato
    • Keyworks: VNFP, MICP (mixed integer convex programming), Viterbi-based
    • Objective: Minimize the cost of resources of the InPs and maximizing the reliability of the service
    • Link: IEEE Xplore

  12. Provably Efficient Algorithms for Placement of Service Function Chains with Ordering Constraints

    • Publication: INFOCOM 2018 (CCF-A)
    • Authors: Ziyue Luo, Chuan Wu
    • Keyworks: VNFP, Equivalence with Hitting Set, Naive and Faster Greedy, LP-Rounding, DP (Dynamic Programming)
    • Objective: Minimize the total deployment cost
    • Link: paper

  13. Toward Profit-Seeking Virtual Network Embedding

    • Publication: INFOCOM 2014 (CCF-A)
    • Authors: Long Gong, Yonggang Wen, Zuqing Zhu and Tony Lee
    • Keyworks: VNE, GRC (Global Resource Control)
    • Objective: Maximize the revenue-to-cost ratio and acceptance ratio
    • Link: IEEE Xplore

Meta-Heuristic

  1. A Constructive Particle Swarm Optimizer for Virtual Network Embedding

    • Publication: TNSE 2020 (JCR-Q1)
    • Authors: Yongqiang Gao; Haibing Guan; Zhengwei Qi; Yang Hou; Liang Liu
    • Keyworks: VNE, CPSO (Constructive Particle Swarm Optimizer)
    • Objective: MinimiziE the cost of bandwidth for embedding the VN
    • Link: IEEE Xplore

  2. A Multi-objective Ant Colony System algorithm for Virtual Machine Placement in Cloud Computing

    • Publication: JCSS 2013 (CCF-B)
    • Authors: Panpan Jin; Xincai Fei; Qixia Zhang; Fangming Liu; Bo Li
    • Keyworks: VNFP, ACS (Ant Colony System), Multi-objective
    • Objective: Minimize total resource wastage and power consumption
    • Link: IEEE Xplore

  3. Virtual Network Embedding through Topology Awareness and Optimization

    • Publication: CN 2012 (CCF-B)
    • Authors: Xiang Cheng, Sen Su, Zhongbao Zhang, Kai Shuang, Fangchun Yang, Yan Luo, Jie Wang
    • Keyworks: VNFP, PSO (Particle Swarm Optimization), Topology decomposition
    • Objective: Minimize total resource wastage and power Consumption
    • Link: IEEE Xplore

Reinforcement learning-based

Basic RL

  1. A Dynamic Reliability-Aware Service Placement for Network Function Virtualization (NFV)

    • Publication: JSAC 2020 (CCF-A)
    • Authors: Zhongxia Yan, Jingguo Ge, Yulei Wu, Liangxiong Li, Tong Li
    • Keyworks: VNFP, Dynamic Reliability-aware, MDP (Markov Deci- sion Process), Viterbi algorithm
    • Objective: Minimize the placement cost and maximize the number of admitted services
    • Link: paper

  2. MUVINE: Multi-Stage Virtual Network Embedding in Cloud Data Centers Using Reinforcement Learning-Based Predictions

    • Publication: JSAC 2020 (CCF-A)
    • Authors: Hiren Kumar Thakkar, Chinmaya Dehury, Prasan Kumar Sahoo
    • Keyworks: VNE, Q-learning, ML(Machine Learning), Multi-Stage
    • Objective: Maximize the server resources utilization and minimizing the number of physical links used
    • Link: paper

  3. A Privacy-Preserving Reinforcement Learning Algorithm for Multi-Domain Virtual Network Embedding

    • Publication: TNSM 2020 (CCF-C)
    • Authors: Davide Andreoletti, Tanya Velichkova, Giacomo Verticale, Massimo Tornatore , Silvia Giordano
    • Keyworks: VNE, Multi-domain, Privacy
    • Objective: /
    • Link: IEEE Xplore

  4. Virtual Network Embedding via Monte Carlo Tree Search

    • Publication: IEEE Trans on Cybernetics 2018 (CCF-B)
    • Authors: Soroush Haeri and Ljiljana Trajkovi´c
    • Keyworks: VNE, MCTS (Monte Carlo Tree Search)
    • Objective: Maximize the profit of InPs (revenue-to-cost and acceptance ratio)
    • Link: paper

  5. An Efficient Algorithm for Virtual Network Function Placement and Chaining

    • Publication: CCNC 2017
    • Authors: Oussama Soualah, Marouen Mechtri, Chaima Ghribi, Djamal Zeghlache
    • Keyworks: VNFP, MCTS (Monte Carlo Tree Search)
    • Objective: Maximize the acceptance rate of provisioning requests
    • Link: paper

Deep RL

  1. Automatic Virtual Network Embedding: A Deep Reinforcement Learning Approach With Graph Convolutional Networks

    • Publication: JSAC 2020 (CCF-A)
    • Authors: Zhongxia Yan, Jingguo Ge, Yulei Wu, Liangxiong Li, Tong Li
    • Keyworks: VNE, A3C (Asynchronous Advantage Actor-Critic), GCN (Graph Convolutional Network)
    • Objective: Minimizing the acceptance ratio and long-term average revenue
    • Link: IEEE Xplore

  2. Optimal VNF Placement via Deep Reinforcement Learning in SDN/NFV-Enabled Networks

    • Publication: JSAC 2020 (CCF-A)
    • Authors: Jianing Pei, Peilin Hong, Miao Pan, Jiangqing Liu, Jingsong Zhou
    • Keyworks: VNFP, DDQN (Double Deep Q Network), BIP (Binary Integer Programming)
    • Objective: Minimize the weighted cost consisting of VNF placement cost, penalty of reject SFCRs and VNFI running cost in every time interval \(\Delta t\)
    • Link: IEEE Xplore

  3. Intelligent VNF Orchestration and Flow Scheduling via Model-Assisted Deep Reinforcement Learning

    • Publication: JSAC 2020 (CCF-A)
    • Authors: Lin Gu, Deze Zeng, Wei Li, Song Guo, Albert Y. Zomaya, Hai Jin
    • Keyworks: VNFS, Latency-awareness, flow, DDPG (Deep Deterministic Policy Gradient)
    • Objective: Maximize the overall network utility with the consideration of end-to-end delay and various cost
    • Link: IEEE Xplore

  4. Virtual Network Function Placement Optimization with Deep Reinforcement Learning

    • Publication: JSAC 2019 (CCF-A)
    • Authors: Ruben Solozabal, Josu Ceberio, Aitor Sanchoyerto, Luis Zabala, Bego Blanco, Fidel Liberal
    • Keyworks: VNFP, PG (Policy Gradient), Seq2Seq (Sequence-to-Sequence)
    • Objective: Minimize the overall power consumption
    • Link: IEEE Xplore

  5. DeepViNE: Virtual Network Embedding with Deep Reinforcement Learning

    • Publication: INFOCOM 2019 (CCF-A)
    • Authors: Mahdi Dolati, Seyedeh Bahereh Hassanpour, Majid Ghaderi, Ahmad Khonsari
    • Keyworks: VNE, DQN (Deep Q Network), Multi-channels Representations
    • Objective: Minimize the VN blocking probability
    • Link: paper

  6. Multi-domain Non-cooperative VNF-FG Embedding: A Deep Reinforcement Learning Approach

    • Publication: INFOCOM 2019 (CCF-A)
    • Authors: Pham Tran Anh Quang, Abbas Bradai, Kamal Deep Singh, Yassine Hadjadj-Aoul
    • Keyworks: VNFP, DDPG (Deep Deterministic Policy Gradient), Multi-domain, Non-cooperative
    • Objective: Maximize the number of allocated VNFs and VLs with the lowest cost
    • Link: paper

  7. Deep Reinforcement Learning based VNF Management in Geo-distributed Edge Computing

    • Publication: ICDCS 2019 (CCF-B)
    • Authors: Lin Gu, Deze Zeng, Wei Li, Song Guo, Albert Y. Zomaya, Hai Jin
    • Keyworks: VNFS, Latency-awareness, flow, DDPG (Deep Deterministic Policy Gradient)
    • Objective: Minimize the end-to-end delays and various operation costs
    • Link: IEEE Xplore

  8. VNE-TD: A virtual network embedding algorithm based on temporal-difference learning

    • Publication: CN 2019 (CCF-B)
    • Authors: Sen Wang, Jun Bi, Jianping Wu, Athanasios V. Vasilakos, Qilin Fan
    • Keyworks: VNFP, TD (Temporal Difference), GRC (Global Resource Control)
    • Objective: Maximize the long-term time-average revenue of the InP
    • Link: ScienceDirect

  9. NFVdeep: adaptive online service function chain deployment with deep reinforcement learning

    • Publication: IWQoS 2019 (CCF-B)
    • Authors: Yikai Xiao, Qixia Zhang, Fangming Liu, Jia Wang, Miao Zhao, Zhongxing Zhang, Jiaxing Zhang
    • Keyworks: VNFP, PG (Policy Gradient), Serialization and Backtracking, Time Slots
    • Objective: Minimize the operation cost of occupied servers and maximize the total throughput of accepted requests
    • Link: paper

Unassorted

They will be classified as soon as possible.

  1. Virtual Network Functions Migration Cost: from Identification to Prediction

    • Publication: CN 2020 (CCF-B)
    • Authors: Rafael de JesusMartins, Cristiano Bonato Both, Juliano Araújo Wickboldt, Lisandro Zambenedett iGranville
    • Keyworks: VNFM, Linear regression
    • Objective: A novel architecture for orchestrating and enforcing multi-domain SFCs
    • Link: ScienceDirect

  2. On cross-domain Service Function Chain orchestration: An architectural framework

    • Publication: CN 2021 (CCF-B)
    • Authors: Nassima Toumi, Olivier Bernier, Djamal-Eddine Meddour, Adlen Ksentini
    • Keyworks: VNFC & VNFP, Multi-domain
    • Objective: A novel architecture for orchestrating and enforcing multi-domain SFCs
    • Link: ScienceDirect

  3. pSMART: A lightweight, privacy-aware service function chain orchestration in multi-domain NFV/SDN

    • Publication: CN 2020 (CCF-B)
    • Authors: Kalpana D. Joshi , Kotaro Kataoka
    • Keyworks: VNFC, Multi-domain, Privacy
    • Objective: Utilize less sensitive information, to reduce privacy and security risks
    • Link: ScienceDirect

  4. End-to-end network slicing for future wireless in multi-region cloud platforms

    • Publication: CN 2020 (CCF-B)
    • Authors: Simona Marinova , Thomas Lin, Hadi Bannazadeh, Alberto Leon-Garcia
    • Keyworks: VNFC & VNFP, Multi-domain, E2E (End-to-end) network slicing
    • Objective: /
    • Link: ScienceDirect
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