Resource Allocation For NFV-enabled Multicasting In Mobile Edge Computing

When serving increasing mobile users,the traditional cloud computing paradigm based on the data center will lead to backbone network congestion and cause high response delay which is unbearable for some real-time applications.Mobile Edge Computing(MEC)migrates the computing tasks of mobile applications from the data center to the edge of network by deploying small-scale server clusters near base stations that are within the proximity of mobile user,reducing response delay for real-time applications.Providing network services for mobile applications using Network Function Virtualization(NFV)technology in MEC can ensure the security of data transmission and improve the quality of service.However,unlike data center,providing network services using NFV in MEC faces additional challenges: the server clusters in MEC are geographically distributed,with limited computing resources,and mobile users have strict service quality requirements and strong mobility,which puts forward more stringent requirements on the placement of virtual network functions.To this end,considering providing network services for NFV-enabled multicasting in MEC networks,a heuristic algorithm for service function chain embedding and an algorithm based on user mobility prediction for dynamic adjustment are proposed,which improves network service quality by optimizing virtual network function placement and resource allocation.The main work includes:(1)The NFV-enabled multicasting network construction problem and the NFV-enabled multicasting network dynamic adjustment problem are defined formally,which is modeled using Integer Linear Programming.It is proved that the above two problems are NP-hard problem.(2)A two-stage heuristic algorithm for the problem of NFV-enabled multicasting network construction is proposed,which is used to determine the placement of virtual network function and the routing path of multicasting flow.The first stage of the algorithm finds the embedding scheme of the service function chain under the given delay constraint,minimizing the construction cost.The second stage determines the placement of the last network function in the service function chain and the routing path between that location and all destinations,minimizing the delay variations.It is proved that the algorithm can find a feasible solution to the construction problem within a better time complexity.The performance of the proposed algorithm on different network topologies is evaluated through experiments.(3)A heuristic algorithm based on user mobility prediction for the dynamic adjustment of the constructed multicasting network is proposed.This algorithm uses an Ordinary Differential Equation Recurrent Neural Network to predict the user’s next position based on the user’s historical trajectories,and dynamically adjust the deployed service function chain and multicasting tree using the prediction result.The performance of the different mobility prediction models and the user experience improvement brought by dynamic adjustment are evaluated through experiments.The results show that the mobile prediction model proposed improves the accuracy of 4.91%～11.40% and the absolute distance error of 56m～264m compared with other models.The dynamic adjustment strategy proposed can reduce the probability of service quality violation by 41.03%～87.5% compared with no adjustment strategy,and reduce the backup cost by 6.83%～9.66% compared with the adjustment which do not reduce backup cost.In short,the heuristic algorithm proposed can determine the embedding strategy of the NFV service function chain and the routing path within a limited time,minimizing the construction cost of the multicasting network while ensuring the quality of service.The proposed user mobility prediction model based on the Ordinary Differential Recurrent Neural Network can effectively predict the location of the user at a certain moment.The dynamic adjustment algorithm based on user mobility prediction can proactively reconstruct the NFVbased multicasting network to improve the user experience after the user moves.The contributions can effectively improve the quality of NFV-based multicasting in the MEC network,while reducing the construction cost of NFV-based multicasting network.