Optimizing power by selective IP card shutdown using transport slicing
career value
201K/year
🤖 AI Summary
This study addresses the escalating energy consumption challenges in future 6G and AI-driven networks by proposing an IP-based dual-slice energy-saving architecture. The framework dynamically switches between a high-performance Day Slice during peak traffic periods and an energy-efficient Night Slice during off-peak hours, leveraging time-aware slice orchestration and traffic engineering to ensure service continuity. Innovatively integrating multi-objective optimization with line-card-level dynamic power gating, the approach jointly optimizes energy consumption and latency while satisfying diverse QoS requirements across multiple services. Experimental evaluations on the SNDlib india35 topology using Pareto-front evolutionary algorithms—including NSGA-II, CTAEA, and AGE-MOEA—demonstrate that over 40% of line cards can be powered down during low-load periods while maintaining end-to-end latency for critical services below 7 ms, thereby achieving substantial energy efficiency gains.
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
energy efficiency
network slicing
6G networks
line-card shutdown
AI-driven networks
Innovation
Methods, ideas, or system contributions that make the work stand out.
network slicing
energy efficiency
multi-objective optimization
line-card shutdown
6G networks
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