RQAP: Resource and QoS Aware Placement of Service Function Chains in NFV-Enabled Networks

IEEE TRANSACTIONS ON SERVICES COMPUTING(2023)

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摘要
Network Functions Virtualization (NFV), which decouples network functions from the underlying hardware, has been regarded as an emerging paradigm to provide flexible virtual resources for various applications through the ordered interconnection of Virtual Network Functions (VNFs), in the form of Service Function Chains (SFCs). In order to achieve the desired performance as dedicated hardware, how to efficiently deploy SFCs in NFV-enabled networks with limited resources is still a tremendous challenge. In this article, RQAP, an effective Resource and Quality of Service (QoS) Aware SFC Placement approach is proposed to mitigate this issue with the QoS-guaranteed service provisioning at acceptable resource consumption. With the Markov property of VNFs, the resource and QoS aware placement of SFCs is modeled as a Markov-chain-based optimization problem, where the set of all possible placement states on diverse nodes is regarded as a state space in the Markov chain and each state is jointly determined by the initial state and transition matrices. Furthermore, the SFCs associated with traffic requests are re-sorted so as to efficiently instantiate VNFs of the same type in a resource-saving manner. On this basis, an efficient Backward-Viterbi-based heuristic mechanism is presented to conduct the optimal VNF placement in Markov chain space, with the aim of consumed-resource reduction, along with the QoS-based instantiation of virtual links between adjacent VNFs. Simulation results conducted in several scenarios demonstrate that RQAP can significantly achieve a trade-off between resource consumption optimization and QoS guarantee. Besides, the results show that our proposed approach can also effectively improve the SFC acceptance ratio and achieve desirable load balancing and scalability.
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关键词
Network function virtualization (NFV),service function chains (SFCs),quality of service (QoS),Markov chain
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