No Traffic to Cry: Traffic-Oblivious Link Deactivation for Green Traffic Engineering

TL;DR

The paper tackles energy-efficiency in backbone networks by proposing a traffic-oblivious Green TE approach that guarantees routability under any down-scaled traffic. It defines TOCA on a bidirected graph with bundled connections and develops a $\max\big(\frac{1}{\varrho\,\lambda_{ ext{min}}},2\big)$-approximation via LP rounding, plus post-processing heuristics to improve solution quality. The method avoids frequent reconfigurations and scales to realistic backbone topologies, with experiments showing near-optimal solution values and favorable computation times, while maintaining low MLU under varied conditions. This work provides a practical framework for energy savings in core networks, bridging rigorous approximation theory with deployment-ready algorithms, and opens avenues to tailor the model to hardware power profiles and SR-era routing constraints.

Abstract

As internet traffic grows, the underlying infrastructure consumes increasing amounts of energy. During off-peak hours, large parts of the networks remain underutilized, presenting significant potential for energy savings. Existing Green Traffic Engineering approaches attempt to leverage this potential by switching off those parts of the networks that are not required for the routing of specific traffic matrices. When traffic changes, the approaches need to adapt rapidly, which is hard to achieve given the complexity of the problem. We take a fundamentally different approach: instead of considering a specific traffic matrix, we rely on a traffic-oblivious routing scheme. We discuss the NP-hard problem of activating as few connections as possible while still guaranteeing that any down-scaled traffic matrix $\varrho\cdot T$ can be routed, where $\varrho \in (0,1)$ and $T$ is any traffic matrix routable in the original network. We present a $\max(\frac{1}{\varrho\cdotλ_{\text{min}}},2)$-approximation algorithm for this problem, with $λ_{\text{min}}$ denoting the minimum number of connections between any two connected routers. Additionally, we propose two post-processing heuristics to further improve solution quality. Our evaluation shows that we can quickly generate near-optimal solutions. By design, our method avoids the need for frequent reconfigurations and offers a promising direction to achieve practical energy savings in backbone networks.

Figures (4)

• Figure 1: Traffic on an average day in 2022
• Figure 2: A network topology showcasing the non-monotonicity of optimal TOCA solutions w.r.t. $\varrho$. All bidirected edges have capacity 1 and consist of one connection. For $\varrho_1 \in (\frac{1}{2}, \frac{2}{3}\mathclose]$, the unique optimal solution consists of all black edges (both solid and dashed). For $\varrho_2 = \frac{1}{2}$, the unique optimal solution consists of all solid edges (both black and gray). By replacing all edges with arcs directed from left to right we obtain an analogous instance that shows the non-monotonicity of MMCFS solutions.
• Figure 3: Results of the experimental evaluation
• Figure : ILP \ref{['eq:ilp_mcfs_undirected']}: ILP formulation for TOCA

Theorems & Definitions (11)

• Theorem 1
• Theorem 2
• proof
• Theorem 3
• proof
• Lemma 1
• proof
• Theorem 4
• proof
• Theorem 5