ATRO: A Fast Algorithm for Topology Engineering of Reconfigurable Datacenter Networks

TL;DR

ATRO is a fast, unified algorithm for optimizing the logical topology and routing in reconfigurable datacenter networks, improving efficiency and performance over prior methods.

Contribution

It introduces a unified framework supporting both one-hop and multi-hop topology optimization with guaranteed global optimality in the one-hop case and efficient iterative solutions in the multi-hop case.

Findings

Outperforms prior approaches in runtime.

Achieves higher maximum link utilization.

Supports solver-free traffic engineering methods.

Abstract

Reconfigurable data center networks (DCNs) enhance traditional architectures with optical circuit switches (OCSs), enabling dynamic reconfiguration of inter-pod links, i.e., the logical topology. Optimizing this topology is crucial for adapting to traffic dynamics but is challenging due to its combinatorial nature. The complexity increases further when demands can be distributed across multiple paths, requiring joint optimization of topology and routing. We propose Alternating Topology and Routing Optimization (ATRO), a unified framework that supports both one-hop topology optimization (where traffic is routed via direct paths) and multi-hop joint optimization (where routing is also optimized). Although these settings differ in constraints, both are combinatorially hard and challenge solver-based methods. ATRO addresses both cases efficiently: in the one-hop case, it guarantees the global optimum via an accelerated binary search; in the multi-hop case, it alternates between topology and routing updates, with routing steps optionally accelerated by existing traffic engineering (TE) methods. ATRO supports warm-starting and improves solution quality monotonically across iterations. ATRO remains competitive even when paired with solver-free TE methods, forming a fully solver-free optimization pipeline that still outperforms prior approaches in runtime and maximum link utilization across diverse workloads.