Self-Adjusting Ego-Trees Topology for Reconfigurable Datacenter Networks

TL;DR

This paper introduces GreedyEgoTrees, a dynamic algorithm for reconfigurable datacenter networks that adapts topology based on traffic patterns, improving efficiency over static designs.

Contribution

The paper proposes a novel greedy algorithm for demand-aware network reconfiguration, demonstrating theoretical advantages and practical improvements in path length reduction.

Findings

GreedyEgoTrees outperforms static and other dynamic algorithms.

Significant reduction in average path length for real traffic traces.

The algorithm has desirable theoretical properties.

Abstract

State-of-the-art topologies for datacenters (DC) and high-performance computing (HPC) networks are demand-oblivious and static. Therefore, such network topologies are optimized for the worst-case traffic scenarios and can't take advantage of changing demand patterns when such exist. However, recent optical switching technologies enable the concept of dynamically reconfiguring circuit-switched topologies in real-time. This capability opens the door for the design of self-adjusting networks: networks with demand-aware and dynamic topologies in which links between nodes can be established and re-adjusted online and respond to evolving traffic patterns. This paper studies a recently proposed model for optical leaf-spine reconfigurable networks. We present a novel algorithm, GreedyEgoTrees, that dynamically changes the network topology. The algorithm greedily builds ego trees for nodes in the network, where nodes cooperate to help each other, taking into account the global needs of the network. We show that GreedyEgoTrees has nice theoretical properties, outperforms other possible algorithms (like static expander and greedy dynamic matching) and can significantly improve the average path length for real DC and HPC traces.