Improved Scalability of Demand-Aware Datacenter Topologies With Minimal Route Lengths and Congestion
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.
Top- Pacut, Maciej
- Dai, Wenkai
- Labbe, Alexandre
- Foerster, Klaus-Tycho
- Schmid, Stefan
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 |
Export |