Resource allocation strategy based on service function Chaining in Multi-Access edge computing network

Abstract

The Multi-access Edge Computing (MEC) network enhances the processing capability of Internet of Things (IoT) terminals by deploying 3C resources (computing resources, cache resources, and communication resources) at the IoT edge side, effectively improving the quality of service (QoS) of IoT applications. The majority of current MEC network research focuses on task offloading and energy management, with few studies looking at resource allocation from a service provisioning perspective. In this paper, we focus on the resource allocation problem when processing multiple tasks requested by IoT terminal devices in the MEC network. Firstly, we transform various tasks requested by IoT end devices into an SFC-based resource allocation problem. Then, we model the problem as a graph theory-based virtualized network function (VNF) node deployment cost optimization problem. Furthermore, we propose two resource allocation and deployment strategies by jointly considering the computing cost and security cost in VNF node deployment. Finally, extensive simulation experiments show that the proposed method can significantly improve the success rate of SFC task processing by more than 30% and network resource utilization by more than 20% when compared to other deployment methods for processing multiple SFC tasks with dynamic requests.

Keywords

• multi-access edge computing (MEC)
• service function chain(SFC)
• network function virtualization(NFV)
• deep reinforcement learning(DRL)
• Resource allocation