Delay guaranteed SFC placement with VNF parallelization in multidomain IoT networks

在多域物联网网络中,通过 VNF 并行化实现延迟保证的 SFC 部署

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Abstract

As an emerging network technology, Network Function Virtualization (NFV) enables network functions decoupling from dedicated hardware by replacing traditional middleboxes with software implemented Virtual Network Functions (VNFs). In NFV-enabled Internet of Things (IoT) networks, each IoT service can be represented as an ordered sequence of VNFs, referred to as Service Function Chain (SFC). Through NFV, operating expenditure and capital expenditure can be significantly reduced, thereby achieving flexible provisioning of IoT services. However, with the arriving of 6G era, the network scale of IoTs continuously expands, and service requirements of IoT users become more diversified. Particularly, 6G enabled IoT services have stringent delay requirements. How to efficiently place the SFCs in multi-domain IoT networks to satisfy the specific delay requirements while guaranteeing quality of service becomes a serious challenge. To this end, in this paper, we investigate the problem of delay guaranteed SFC placement in multi-domain IoT networks. Specifically, by taking in account QoS requirements and VNF dependency relationships, we formulate the problem of delay guaranteed SFC placement in multi-domain IoT networks as a multi-objective optimization model to maximize service acceptance ratio and minimize operational cost, while satisfying the delay requirements of SFC requests. To solve the problem, we further design a Delay Guaranteed heuristic SFC Placement (DGSP) algorithm with VNF parallelization. In the proposed DGSP algorithm, the VNFs without dependency relationships are placed in parallel in an adaptive and cost efficient manner, and virtual link mapping is performed based on the shortest path algorithm. Finally, we conduct simulation experiments for performance evaluation, and simulation results demonstrate the proposed DGSP algorithm can get higher service acceptance ratio and lower operational cost than comparison algorithms.

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