Network function virtualization (NFV) and content caching are two promising technologies that hold great potential for network operators and designers. This paper optimizes the deployment of NFV and content caching in 5G networks and focuses on the associated power consumption savings. In addition, it introduces an approach to combine content caching with NFV in one integrated architecture for energy aware 5G networks. A mixed integer linear programming (MILP) model has been developed to minimize the total power consumption by jointly optimizing the cache size, virtual machine (VM) workload, and the locations of both cache nodes and VMs. The results were investigated under the impact of core network virtual machines (CNVMs) inter-traffic. The result show that the optical line terminal (OLT) access network nodes are the optimum location for content caching and for hosting VMs during busy times of the day whilst IP over WDM core network nodes are the optimum locations for caching and VM placement during off-peak time. Furthermore, the results reveal that a virtualization-only approach is better than a caching-only approach for video streaming services where the virtualization-only approach compared to caching-only approach, achieves a maximum power saving of 7% (average 5%) when no CNVMs inter-traffic is considered and 6% (average 4%) with CNVMs inter-traffic at 10% of the total backhaul traffic. On the other hand, the integrated approach has a maximum power saving of 15% (average 9%) with and without CNVMs inter-traffic compared to the virtualization-only approach, and it achieves a maximum power saving of 21% (average 13%) without CNVMs inter-traffic and 20% (average 12%) when CNVMs inter-traffic is considered compared with the caching-only approach. In order to validate the MILP models and achieve real-time operation in our approaches, a heuristic was developed.
翻译:网络功能虚拟化( NFVV) 和内容缓存是两种前景良好的技术, 给网络操作者和设计者带来巨大潜力。 本文优化了在5G 网络中部署 NFV 和内容缓冲, 并侧重于相关的电耗节省。 此外, 它引入了一种方法, 将内容缓存与NFV结合到一个能源意识 5G 网络的综合架构中。 已经开发了一个混合整流线性编程模型( MILP), 通过联合优化缓存大小、 虚拟机器( VM) 工作量以及缓存节点节点和 VMSM 。 在核心网络虚拟机器( CNVVM) 和内容在5GMS 间缓存的影响下对结果进行了调查。 光线性终端终端( OLT) 访问网节点是内容缓存的最佳地点, 在忙碌时, IP 20DMT 核心网络节点被认为是缓存和 VM 的最好地点。 此外, 虚拟化方法比 CD- 5NVM 内部节流( C- sal- musal- mess) 实现 10%