The increasing energy demands of upcoming sixth-generation (6G) mobile networks and networks supporting AI applications pose significant challenges for network operators in terms of operational costs and environmental impact. To address these challenges, this paper proposes a novel IP-based network slicing strategy that optimizes energy efficiency through a dual-slice approach. The proposed solution consists of a Day Slice, designed to meet high-performance requirements during peak traffic hours, and a Night Slice, optimized for energy savings by deactivating excess line-cards in card-based routers during periods of low traffic demand. The traffic is switched between the Day and Night Slices at predefined times, assuming appropriate traffic engineering mechanisms are in place to minimize disruption and support session continuity. We apply Pareto-based evolutionary algorithms (NSGA-II, CTAEA, and AGE-MOEA) to jointly optimize energy consumption and latency. Experiments conducted on the SNDlib india35 topology demonstrate that multi-objective optimization can deactivate over 40% of line cards during low-traffic periods, providing significant energy savings while maintaining acceptable performance. Additionally, a multi-service extension using AGE-MOEA introduces differentiated QoS constraints, maintaining latency below 7 ms for premium traffic while preserving substantial energy savings.

翻译：针对即将到来的第六代（6G）移动网络及支持AI应用的网络日益增长的能耗需求，运营商正面临运营成本与环境影响方面的严峻挑战。为解决这些问题，本文提出一种基于IP的新型网络切片策略，通过双切片方法优化能效。该方案包含"日间切片"（针对高峰时段高性能需求设计）和"夜间切片"（通过低流量时段关停基于板卡的路由器中冗余线路卡实现节能优化）。在预设时间点，借助适当的流量工程机制实现日/夜间切片间的流量切换，以最大程度减少中断并保障会话连续性。我们采用基于帕累托的进化算法（NSGA-II、CTAEA和AGE-MOEA）联合优化能耗与延迟。基于SNDlib india35拓扑的实验表明，多目标优化可在低流量时段关停超过40%的线路卡，在保持可接受性能的同时实现显著节能。此外，采用AGE-MOEA的多业务扩展引入了差异化QoS约束，在保持大量节能效果的同时，将优先流量延迟控制在7毫秒以下。