Migration and replication of virtual network functions (VNFs) are well-known mechanisms to face dynamic resource requests in Internet Service Provider (ISP) edge networks. They are not only used to reallocate resources in carrier networks, but in case of excessive traffic churns also to offloading VNFs to third party cloud providers. We propose to study how traffic forecasting can help to reduce the number of required migrations and replications when the traffic dynamically changes in the network. We analyze and compare three scenarios for the VNF migrations and replications based on: (i) the current observed traffic demands only, (ii) specific maximum traffic demand value observed in the past, or (iii) predictive traffic values. For the prediction of traffic demand values, we use an LSTM model which is proven to be one of the most accurate methods in time series forecasting problems. Based the traffic prediction model, we then use a Mixed-Integer Linear Programming (MILP) model as well as a greedy algorithm to solve this optimization problem that considers migrations and replications of VNFs. The results show that LSTM-based traffic prediction can reduce the number of migrations up to 45\% when there is enough available resources to allocate replicas, while less cloud-based offloading is required compared to overprovisioning.
翻译:虚拟网络功能(VNFs)的迁移和复制是人们熟知的应对互联网服务提供商边缘网络动态资源需求的机制,不仅用于重新分配承运人网络的资源,而且用于将VNFs卸载到第三方云端提供者的过度交通量,我们提议研究交通预测如何有助于在网络交通动态变化时减少所需的迁移和复制数量。我们分析和比较VNF迁移和复制的三种情景,其依据是:(一) 仅目前观察到的交通需求,(二) 过去观察到的特定最大交通需求值,或(三) 预测交通值。为了预测交通需求值,我们使用LSTM模式,这已证明是时间序列预测问题中最准确的方法之一。基于交通预测模型,我们随后使用混合Interger线性规划模型以及贪婪算法来解决这一优化问题,即考虑VNFs的迁移和复制。结果显示,LSTM的交通需求最大值值可以减少可调用的资源数量,同时将可调用量在45层之上。