AI

Minimal Rewiring: Efficient Live Expansion for Clos Data Center Networks

Abstract

Clos topologies have been widely adopted for large-scale data center networks (DCNs), but it has been difficult to support incremental expansions of Clos DCNs. Some prior work has assumed that it is impossible to design DCN topologies that are both well-structured (non-random) and incrementally expandable at arbitrary granularities.

We demonstrate that it is indeed possible to design such networks, and to expand them while they are carrying live traffic, without incurring packet loss. We use a layer of patch panels between blocks of switches in a Clos network, which makes physical rewiring feasible, and we describe how to use integer linear programming (ILP) to minimize the number of patch-panel connections that must be changed, which makes expansions faster and cheaper. We also describe a block-aggregation technique that makes our ILP approach scalable.

We tested our "minimal-rewiring" solver on two kinds of fine-grained expansions using 2250 synthetic DCN topologies, and found that the solver can handle 99% of these cases while changing under 25% of the connections. Compared to prior approaches, this solver (on average) reduces the number of "stages" per expansion by about 3.1X -- a significant improvement to our operational costs, and to our exposure (during expansions) to capacity-reducing faults.