Solving Max-Min Fair Resource Allocations Quickly on Large Graphs

TL;DR

This paper introduces faster, more general algorithms for max-min fair resource allocation on large graphs, improving efficiency and fairness over prior methods, with practical deployment in Azure's WAN traffic engineering.

Contribution

It converts a sequence of optimizations into a single fast optimization and generalizes waterfilling to multi-path scenarios, enhancing scalability and fairness.

Findings

Algorithms outperform prior techniques in speed and fairness.

Deployed in Azure, achieving approximately 3x speedup.

Some algorithms provide theoretical guarantees with bounded unfairness.

Abstract

We consider the max-min fair resource allocation problem. The best-known solutions use either a sequence of optimizations or waterfilling, which only applies to a narrow set of cases. These solutions have become a practical bottleneck in WAN traffic engineering and cluster scheduling, especially at larger problem sizes. We improve both approaches: (1) we show how to convert the optimization sequence into a single fast optimization, and (2) we generalize waterfilling to the multi-path case. We empirically show our new algorithms Pareto-dominate prior techniques: they produce faster, fairer, and more efficient allocations. Some of our allocators also have theoretical guarantees: they trade off a bounded amount of unfairness for faster allocation. We have deployed our allocators in Azure's WAN traffic engineering pipeline, where we preserve solution quality and achieve a roughly $3\times$ speedup.