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Customizing Service Path Based on Polymorphic Routing Model in Future Networks

Customizing Service Path Based on Polymorphic Routing Model in Future Networks
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摘要 The current Internet has evolved during the last decade to a global provider of diverse applications. However, the underlying structure of routing and addressing has not evolved in the same pace and is somewhat inflexible. How to provide diverse routing services, support emerging communication paradigms based on limited and definite network resources has become an urgent challenge. This paper investigates the adaptive matching between routing and application through network function decomposition and composition, and proposes a polymorphic routing model to support diverse applications and emerging communication paradigms. The model splits complex routing functions into its constituents, and derives customized routing mechanisms supporting various applications by composing the routing constituents. The derivation process is modeled as a Markov Decision Process (MDP), and a polymorphic derivation algorithm is also proposed to derive customized routing instances for diverse applications. The model enables the network to self-adjust routing services dynamically to adapt to the different requirements of applications, supports coexistence of multiple routing modes and communication paradigms, and provides a feasible solution for the network compatibility and evolvement. We describe the key design and demonstrate the feasibility of polymorphic derivation by simulations. We also present case studies that demonstrate key functionalities the polymorphic routing model enables. The current Internet has evolved during the last decade to a global provider of diverse applications. However, the underlying structure of routing and addressing has not evolved in the same pace and is somewhat inflexible. How to provide diverse routing services, support emerging communication paradigms based on limited and definite network resources has become an urgent challenge. This paper investigates the adaptive matching between routing and application through network function decomposition and composition, and proposes a polymorphic routing model to support diverse applications and emerging communication paradigms. The model splits complex routing functions into its constituents, and derives customized routing mechanisms supporting various applications by composing the routing constituents. The derivation process is modeled as a Markov Decision Process(MDP), and a polymorphic derivation algorithm is also proposed to derive customized routing instances for diverse applications. The model enables the network to self-adjust routing services dynamically to adapt to the different requirements of applications, supports coexistence of multiple routing modes and communication paradigms, and provides a feasible solution for the network compatibility and evolvement. We describe the key design and demonstrate the feasibility of polymorphic derivation by simulations. We also present case studies that demonstrate key functionalities the polymorphic routing model enables.
出处 《China Communications》 SCIE CSCD 2019年第7期207-228,共22页 中国通信(英文版)
基金 supported in part by the Cernet Network (NGII20160103) National Natural Science Foundation of China, National Natural Science Foundation of China(No.61672471) Fundamental Research Funds for the He’nan Province University (No.17KYYWF0202) He’nan Province University science and technology innovation team(No.18IRTSTHN012) Plan For Scientific Innovation Talent of Henan Province (No.184200510010) Zhengzhou University of Light Industry Doctoral Fund (2016BSJJ041) funding
关键词 POLYMORPHIC base state ROUTING model MDP REINFORCEMENT learning polymorphic base state routing model MDP reinforcement learning
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