面向5G-ICN切片的分层缓存策略

Hierarchical Caching Strategy for 5G-ICN Slicing

多接入边缘计算(Multiple-Access Edge Computing,MEC)和网络切片在5G网络中扮演了重要角色。网络切片的灵活可编程性和MEC的去中心化促进了信息中心网络(Information-Centric Network,ICN)的部署。但是,ICN中缓存资源分配的位置和数量严重影响了运营商的收益。为此,首先将移动虚拟网络运营商(Mobile Virtual Network Operator,MVNO)和多个内容提供商(Content Provider,CP)建模为一主多从的Stackelberg博弈;其次,将MVNO内部的缓存资源分配建模为一个联合缓存成本和传输能耗的混合整数规划问题,并利用改进的精英遗传算法得到最佳位置;最后,通过博弈迭代算法得到运营商的最优定价和CP的最优放置数量。仿真结果表明,该算法具有较高的有效性以及较好的缓存资源分配能力,能够降低传输能耗和缓存成本,与贪婪搜索(Iteration Greedy Search,IGS)算法、联合缓存分配和延时控制(Joint Cache Allocation and Delay Control,JCADC)算法相比传输能耗分别节省了5%、11%,运营商的收益分别提高了32%、44%。

Multiple-access edge computing(MEC)and network slicing play an essential role in the 5G networks.The flexible programmability of network slicing and the decentralization of the MEC promote the deployment of information-centric network(ICN).However,the location and quantity of caching resources allocation in the ICN seriously affect the operator’s revenue.Therefore,the mobile virtual network operator(MVNO)and multiple content providers(CPs)are modeled as a Stackelberg game with one leader and multiple followers.Secondly,the allocation of caching resources in the MVNO is modeled as a mixed-integer programming problem that combines cache cost and transmission energy consumption,and the optimal location is obtained using an improved elite genetic algorithm.Finally,through the game iterative algorithm,the optimal pricing of the MVNO and the optimal cache quantity of CPs are obtained.The simulation results show that the algorithm has higher effectiveness and better ability for caching resource allocation.The proposed algorithm can reduce transmission energy consumption and cache cost,and increase the operator’s revenue.Compared with the iteration greedy search(IGS)algorithm,and the joint cache allocation and delay control(JCADC)algorithm,the transmission energy consumption is saved by 5%and 11%,and the revenue of the operator is increased by 32%and 44%,respectively.