Optimizing power by selective IP card shutdown using transport slicing

TL;DR

This paper introduces a dual-slice IP network strategy for 6G that optimizes energy efficiency by selectively shutting down line cards during low traffic periods, using multi-objective evolutionary algorithms.

Contribution

It proposes a novel dual-slice approach combined with Pareto-based algorithms to significantly reduce energy consumption in IP networks while maintaining performance.

Findings

Over 40% of line cards can be deactivated during low traffic periods.

Multi-objective optimization balances energy savings and latency effectively.

Differentiated QoS constraints maintain latency below 7 ms for premium traffic.

Abstract

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.