Improved Scalability of Demand-Aware Datacenter Topologies With Minimal Route Lengths and Congestion

Improved Scalability of Demand-Aware Datacenter Topologies With Minimal Route Lengths and Congestion

Abstract

The performance of more and more cloud-based applications critically depends on the performance of the interconnecting datacenter network. Emerging reconfigurable datacenter networks have the potential to provide an unprecedented throughput by dynamically reconfiguring their topology in a demand-aware manner. This paper studies the algorithmic problem of how to design low-degree and hence scalable datacenter networks that are optimized toward the current traffic they serve. Our main contribution is a novel network design which provides asymptotically minimal route lengths and congestion. In comparison to prior work, our design reduces the degree requirements by a factor of four for sparse demand matrices. We further show that the problem is already NP-hard for tree-shaped demands, but permits a 2-approximation on the route lengths and a 6-approximation for congestion. We further report on a small empirical study on Facebook traces.

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Authors
  • Pacut, Maciej
  • Dai, Wenkai
  • Labbe, Alexandre
  • Foerster, Klaus-Tycho
  • Schmid, Stefan
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Supplemental Material
Shortfacts
Category
Paper in Conference Proceedings or in Workshop Proceedings (Paper)
Event Title
39th International Symposium on Computer Performance, Modeling, Measurements and Evaluation
Divisions
Communication Technologies
Subjects
Theoretische Informatik
Rechnerperipherie, Datenkommunikationshardware
Event Location
Virtual Conference
Event Type
Conference
Event Dates
8-12 Nov 2021
Date
November 2021
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