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arXiv · 2310.09699

Solving Max-Min Fair Resource Allocations Quickly on Large Graphs

Pooria Namyar·Behnaz Arzani·Srikanth Kandula·Santiago Segarra·Daniel Crankshaw·Umesh Krishnaswamy·Ramesh Govindan·Himanshu Raj
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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.

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Pooria Namyar, Behnaz Arzani, Srikanth Kandula, Santiago Segarra, Daniel Crankshaw, Umesh Krishnaswamy, Ramesh Govindan, Himanshu Raj. 2023-10-15. Solving Max-Min Fair Resource Allocations Quickly on Large Graphs. https://arxiv.org/abs/2310.09699

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