Toward Self-Adjusting k-Ary Search Tree Networks
Feder, E., Paramonov, A., Mavrin, P. , Salem, I., Schmid, S. & Aksenov, V.
ORCID: 0000-0001-9134-5490 (2024).
Toward Self-Adjusting k-Ary Search Tree Networks.
In:
2024 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW).
2024 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), 27-31 May 2024, San Francisco, CA, USA.
doi: 10.1109/ipdpsw63119.2024.00209
Abstract
Datacenter networks are becoming increasingly flexible with the incorporation of new optical communication technologies, such as optical circuit switches, enabling self-adjusting topologies that can adapt to the traffic pattern in a demand-aware manner. In this paper, we take the first steps toward demand-aware and self-adjusting k-ary tree networks. These are more powerful generalizations of existing binary search tree networks (like SplayNet [14]), which have been at the core of self-adjusting network (SAN) designs. k-ary search tree networks are a natural generalization offering nodes of higher degrees, reduced route lengths, and local routing in spite of reconfigurations (due to maintaining the search property). Our main results are algorithms for static k-ary tree networks and two online heuristics for self-adjusting k-ary tree networks.
| Publication Type: | Conference or Workshop Item (Paper) |
|---|---|
| Additional Information: | © Copyright 2024 IEEE. |
| Publisher Keywords: | self-adjusting networks, k-ary trees, online algorithms, dynamic programming |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Departments: | School of Science & Technology School of Science & Technology > Department of Computer Science |
| SWORD Depositor: |
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