Service Function Chaining in NFV-Enabled Edge Networks with Natural Actor-Critic Deep Reinforcement Learning

Wang, Ruijie; Li, Junhuai; Wang, Kan; Liu, Xuan; Lit, Xuan

doi:10.1109/iccc52777.2021.9580255

Cited by 3 publications

(1 citation statement)

References 40 publications

(68 reference statements)

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“…For instance, in [32], RL is adopted for VNF-SC deployment in Elastic optical networks with the objective of load balancing and minimising service delay. In [33], the focus is on minimising end-to-end delay in NFV-enabled networks. The work in [34] incorporates RL and block-chain for cost effective and secure SFC deployment, while the work in [35] focuses on traffic forwarding in SFC chains.…”

Section: Related Workmentioning

confidence: 99%

A Reinforcement Learning Approach for Virtual Network Function Chaining and Sharing in Softwarized Networks

Kibalya

Serrat-Fernández

Gorricho

et al. 2022

IEEE Trans. Netw. Serv. Manage.

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Cognizant of the ease with which softwarized functions can be dynamically scaled according to real time resource requirements, and the fact that multiple services can have common VNFs in their chaining, this paper tackles the problem of cost effective deployment of online services from the perspective of sharing their VNF instances. First, we formally formulate the deployment problem under VNFs sharing. Secondly, given the NP-hard nature of the above problem, we propose a reinforcement learning (RL) algorithm capable of making intelligent placement decisions while considering multiple conflicting costs. Costs of transmission, VNF instantiation or energy consumption, among others. Thanks to the intelligence of the RL algorithm, simulation results show that the performance of the proposed algorithm is within a 14% margin and similar to an optimal solution in terms of request provisioning cost and acceptance ratio, respectively. Moreover, the algorithm results in more than a 20% and a 70% improvement in terms of request deployment cost and time compared to a state-of-the-art algorithm, and up to more than a 40% improvement in terms of cost compared to an algorithm that greedily minimizes the transmission or VNF activation costs.

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