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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
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