计算机科学 ›› 2021, Vol. 48 ›› Issue (12): 324-330.doi: 10.11896/jsjkx.201100159
王珂1, 曲桦1,2, 赵季红2,3
WANG Ke1, QU Hua1,2, ZHAO Ji-hong2,3
摘要: 随着网络虚拟化技术的发展,多域网络中的服务功能链部署为服务功能链优化部署问题带来了新的挑战。传统的部署方法通常对单一目标进行优化,不适用于多目标优化问题,且无法对优化目标间权重进行衡量及平衡。因此,为了对大规模服务功能链部署请求下的时延、网络负载均衡性及接受率进行同步优化,提出了一种数据归一化处理方案,并设计了基于强化学习的两步SFC部署算法。该算法以传输时延与负载均衡性为反馈参数,平衡了两者的权重关系,并对其进行了同步优化,同时利用强化学习框架优化了SFC接受率。实验结果表明,所提算法在大规模请求数下,相比时延感知方法时延降低了71.8%,相比多域部署方法接受率提高了4.6%,相比贪心算法平均负载均衡性提高了39.1%,保证了多目标优化效果。
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[1]YI B,WANG X W,LIK Q,et al.A comprehensive survey of Network Function Virtualization[J].Computer Networks,2018,133:212-262. [2]JOSHI K,BENSON T.Network Function Virtualization[J]. IEEE Internet Computing,2016,20(6):7-9. [3]HALPERN J,PIGNATARO C.Service Function Chaining Ar- chitecture,document RFC 7665 of the IETF Service Function Chaining Working Group[EB/OL].http://datatracker.ietf.org/doc/rfc7665/. [4]LI Y,CHEN M.Software-defined network function virtualization:a survey[J].IEEE Access,2015,3:2542-2553. [5]BERNINI G,GIARDINA P G,SPADARO S,et al.Multi-Do- main Orchestration of 5G Vertical Services and Network Slices[C]//2020 IEEE International Conference On Communications Workshops.Dublin,Ireland,2020:6. [6]WIBOWO F X A,GREGORY M A,AHMED K,et al.Multi-domain Software Defined Networking:Research status and challenges[J].Journal of Network and Computer Applications,2017,87:32-45. [7]CHEN W H,YIN X,WANG Z L,et al.Placement and Routing Optimization Problem for Service Function Chain:State of Art and Future Opportunities[J].arXiv:1910.02613. [8]QU L,ASSI C,SHABAN K.Delay-Aware Scheduling and Resource Optimization With Network Function Virtualization[J].IEEE Transactions on Communications,2016,64(9):3746-3758. [9]ALAMEDDINE H A,QU L,ASSI C.Scheduling Service Function Chains for Ultra-Low Latency Network Services[C]//13th International Conference on Network and Service Management.Tokyo,Japan,2017:9. [10]SUN G,LI Y Y,LI Y,et al.Low-latency orchestration for workflow-oriented service function chain in edge computing[J].Future Generation Computer Systems-the International Journal of Science,2018,85:116-128. [11]GOUAREB R,FRIDERIKOS V,AGHVAMI A H.Virtual Network Functions Routing and Placement for Edge Cloud Latency Minimization[J].IEEE Journal on Selected Areas in Communications,2018,36(10):2346-2357. [12]YE Q,ZHUANG W H,LI X,et al.End-to-End Delay Modeling for Embedded VNF Chains in 5G Core Networks[J].IEEE Internet of Things Journal,2019,6(1):692-704. [13]MIJUMBI R,SERRAT J,GORRICHO J L,et al.Design and evaluation of algorithms for mapping and scheduling of virtual network functions[C]//2015 1st IEEE Conference on Network Softwarization.London,UK,2015:9. [14]ALLEG A,AHMED T,MOSBAH M,et al.Delay-aware VNF placement and chaining based on a flexible resource allocation approach[C]//2017 13th International Conference on Network and Service Management.Tokyo,Japan,2017:7. [15]SHI Z,WU Z H,ZENG Y.A Method of Service Function Chain Arrangement for Load Balancing[C]//9th International Confe-rence on Computer Engineering and Networks.Changsha,China,2019:35-42. [16]HAN H Y,MENG X R,YU Z H,et al.A Service Function Chain Deployment Method Based on Network Flow Theory for Load Balance in Operator Networks[J].IEEE Access,2020,8:93187-93199. [17]XIANG Y F,WU M,WU J,et al.A Load Balancing Method of Virtualization Service Function Chain Based on Time-varying Graphs Integration[J].Journal of Fujian Normal University(Natural Science Edition),2018,34(3):14-20. [18]SUN G,LI Y,LIAO D,et al.Service Function Chain Orchestration Across Multiple Domains:A Full Mesh Aggregation Approach[J].IEEE Transactions on Network and Service Management,2018,15(3):1175-1191. [19]XU Q,GAO D Y,LI TX,et al.Low Latency Security Function Chain Embedding Across Multiple Domains[J].IEEE Access,2018,6:14474-14484. [20]LI G L,ZHOU H C,FENG B H,et al.Context-Aware Service Function Chaining and Its Cost-Effective Orchestration in Multi-Domain Networks[J].IEEE Access,2018,6:34976-34991. [21]DIETRICH D,ABUJODA A,RIZK A,et al.Multi-Provider Service Chain Embedding With Nestor[J].IEEE Transactions on Network And Service Management,2017,14(1):91-105. [22]ABUJODA A,PAPADIMITRIOU P.DistNSE:Distributed Network Service Embedding Across Multiple Providers[C]//8th International Conference on Communication Systems And Networks.Bangalore,India,2016:8. [23]ZHANG C,WANG X W,LI F W,et al.Network Service Chains Deployment Across Multiple SDN Domains[J].International Journal of Communication Systems,2018,31(18):e3826.1-e3826.25. [24]KAUR K,GARG S,KADDOUM G,et al.An Energy-driven Network Function Virtualization for Multi-domain Software Defined Networks[C]//IEEE Conference on Computer Communications.Paris,France,2019:121-126. [25]ZHU G H,LI Q,LIANG S L.Cross-domain mapping algorithm of service function chain based on deep reinforcement learning[J].Application Research of Computers,2021,38(6):1834-1837,1842. |
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