In Network Function Virtualization, the resource demand of the network service evolves with the change of network traffic. VNF dynamic migration has become an effective method to improve network performance. However, for the time-varying resource demand, how to minimize the long-term energy consumption of the network while guaranteeing the Service Level Agreement (SLA) is the key issue that lacks previous research. To tackle this dilemma, this paper proposes an energy-efficient reconfiguration algorithm for VNF based on short-term resource requirement prediction (RP-EDM). Our algorithm uses LSTM to predict VNF resource requirements in advance to eliminate the lag of dynamic migration and determines the timing of migration. RP-EDM eliminates SLA violations by performing VNF separation on potentially overloaded servers and consolidates low-load servers timely to save energy. Meanwhile, we consider the power consumption of servers when booting up, which is existing objectively, to avoid switching on/off the server frequently. The simulation results suggest that RP-EDM has a good performance and stability under machine learning models with different accuracy. Moreover, our algorithm increases the total service traffic by about 15% while ensuring a low SLA interruption rate. The total energy cost is reduced by more than 20% compared with the existing algorithms.

中文翻译：

---

基于短期资源需求预测的节能虚拟网络功能重构策略

在网络功能虚拟化中，网络服务的资源需求随着网络流量的变化而变化。VNF动态迁移已成为提升网络性能的有效方法。然而，对于时变的资源需求，如何在保证服务水平协议（SLA）的同时，最大限度地减少网络的长期能耗，是前人缺乏研究的关键问题。为了解决这个难题，本文提出了一种基于短期资源需求预测（RP-EDM）的 VNF 节能重构算法。我们的算法使用 LSTM 提前预测 VNF 资源需求，以消除动态迁移的滞后并确定迁移的时机。RP-EDM 通过在可能过载的服务器上执行 VNF 分离来消除 SLA 违规，并及时整合低负载服务器以节省能源。同时，我们考虑了服务器开机时的功耗，这是客观存在的，避免频繁开关服务器。仿真结果表明，RP-EDM 在不同精度的机器学习模型下具有良好的性能和稳定性。此外，我们的算法在保证较低的 SLA 中断率的同时，将总服务流量增加了约 15%。与现有算法相比，总能耗降低20%以上。仿真结果表明，RP-EDM 在不同精度的机器学习模型下具有良好的性能和稳定性。此外，我们的算法在保证较低的 SLA 中断率的同时，将总服务流量增加了约 15%。与现有算法相比，总能耗降低20%以上。仿真结果表明，RP-EDM 在不同精度的机器学习模型下具有良好的性能和稳定性。此外，我们的算法在保证较低的 SLA 中断率的同时，将总服务流量增加了约 15%。与现有算法相比，总能耗降低20%以上。