In this talk I will present the vision of self-adjusting networks: communication networks whose physical topology adapts to the traffic pattern it serves, in a demand-aware manner. This vision is reminiscent of biased and self-adjusting datastructures, such as Mehlhorn trees and splay trees. Self-adjusting networks are enabled by emerging reconfigurable optical technologies. I will show that the benefit of self-adjusting networks depends on the amount of “structure” there is in the demand, and present an information-theoretical approach to measure the complexity of traffic traces and derive entropy-based metrics accordingly. I will also present optimal offline and online algorithms to design self-adjusting networks whose performance matches the derived metrics asymptotically.
The talk is primarily based on the following papers:
ReNets: Statically-Optimal Demand-Aware Networks
Chen Avin and Stefan Schmid.
SIAM Symposium on Algorithmic Principles of Computer Systems (APOCS), Alexandria, Virgina, USA, January 2021.
https://www.univie.ac.at/ct/stefan/apocs21renets.pdf
On the Complexity of Traffic Traces and Implications
Chen Avin, Manya Ghobadi, Chen Griner, and Stefan Schmid.
ACM SIGMETRICS, Boston, Massachusetts, USA, June 2020.
https://www.univie.ac.at/ct/stefan/sigmetrics20complexity.pdf
Demand-Aware Network Designs of Bounded Degree
Chen Avin, Kaushik Mondal, and Stefan Schmid.
Distributed Computing (DIST), Springer, 2020.
https://www.univie.ac.at/ct/stefan/dist20journal.pdf
SplayNet: Towards Locally Self-Adjusting Networks
Stefan Schmid, Chen Avin, Christian Scheideler, Michael Borokhovich, Bernhard Haeupler, and Zvi Lotker.
IEEE/ACM Transactions on Networking (TON), Volume 24, Issue 3, 2016.
https://www.univie.ac.at/ct/stefan/ton15splay.pdf
For more details, see the project website:
https://self-adjusting.net/
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Here’s the streaming link: https://telecom-paris.zoom.us/j/94054078947?pwd=VFhyTjIyRWZVYkNzVXp4OXV4c2tTUT09
- ID de réunion : 940 5407 8947
- Code secret : 363501