Research On Schedule Of SFCs In Edge-Core Networks

Multi-access Edge Computing(MEC)offers cloud computing capabilities at the edge of the network.Growing demand for low-latency services requires Service Function Chains(SFCs)to be scaled up beyond MEC network to core network.Combining edge and core networks forms edge-core networks.To adapt to network dynamics and provide low-latency services,being able to migrate SFCs when needed is of paramount importance.However,migration of SFCs in edge-core networks such that average latency is optimized as well as considering resource consumption is an intractable challenge because improper migration of Virtual Network Functions(VNFs)results in failure of meeting the requirements of network policies.This thesis focuses on scheduling of SFCs in edge-core networks to improve the performance of all deployed SFCs.The main contributions are as follows:a)We investigate SFCs in edge-core networks and model the Latency-aware Edge-Core SFCs Migration problem based on open Jackson networks.Two SFC migration algorithms,i.e.,Profit-driven Heuristic Search(PHS)and Average Utilization Based(AUB),are proposed to efficiently optimize average latency of all SFCs in edge-core networks.Extensive evaluation results show that PHS optimizes average latency by 19.5%,while AUB can further reduce average latency by up to 36.9% by allowing a marginally higher number of VNF migrations.b)In practice,traffic patterns in different networks more complex because core network can be shared by multiple edge networks.We use different queuing models for VNFs in edge and core networks in response to different traffic distributions.Aiming at the migration of SFCs in the multi-edge-core networks under network dynamics,a timeseries-based Multi-edge Cooperative SFC Scheduling(MCS)algorithm is proposed to optimize the average latency of SFCs in all edge-core networks.The experimental results show that MCS only averagely migrates 13.8% of the VNFs to optimize the overall average latency by up to 37.3%.