Gemini: Practical Reconfigurable Datacenter Networks with Topology and Traffic Engineering

TL;DR

Gemini is a system that optimizes datacenter network topology and routing based on traffic prediction, enabling cost-effective, reconfigurable, spine-free networks with minimal performance loss.

Contribution

Gemini introduces a joint topology and routing optimization approach using historical traffic data, with robust decision algorithms for infrequent reconfiguration in commodity hardware.

Findings

Supports existing workloads with half the cost of traditional topologies.

Achieves near-optimal performance in low-volatility environments.

Avoids frequent reconfiguration in high-volatility environments.

Abstract

To reduce cost, datacenter network operators are exploring blocking network designs. An example of such a design is a "spine-free" form of a Fat-Tree, in which pods directly connect to each other, rather than via spine blocks. To maintain application-perceived performance in the face of dynamic workloads, these new designs must be able to reconfigure routing and the inter-pod topology. Gemini is a system designed to achieve these goals on commodity hardware while reconfiguring the network infrequently, rendering these blocking designs practical enough for deployment in the near future. The key to Gemini is the joint optimization of topology and routing, using as input a robust estimation of future traffic derived from multiple historical traffic matrices. Gemini "hedges" against unpredicted bursts, by spreading these bursts across multiple paths, to minimize packet loss in exchange for a small increase in path lengths. It incorporates a robust decision algorithm to determine when to reconfigure, and whether to use hedging. Data from tens of production fabrics allows us to categorize these as either low-or high-volatility; these categories seem stable. For the former, Gemini finds topologies and routing with near-optimal performance and cost. For the latter, Gemini's use of multi-traffic-matrix optimization and hedging avoids the need for frequent topology reconfiguration, with only marginal increases in path length. As a result, Gemini can support existing workloads on these production fabrics using a spine-free topology that is half the cost of the existing topology on these fabrics.