SeedTree: A Dynamically Optimal and Local Self-Adjusting Tree
Arash Pourdamghani, Chen Avin, Robert Sama, Stefan Schmid

TL;DR
SeedTree is a new self-adjusting tree data structure that dynamically optimizes itself for demand, supporting local routing and demonstrating both theoretical optimality and practical efficiency on real-world data.
Contribution
We introduce SeedTree, a self-adjusting tree that is dynamically optimal, supports local routing, and is designed for highly dynamic demands, with both theoretical and empirical validation.
Findings
SeedTree achieves dynamic optimality in self-adjusting trees.
It supports local greedy routing efficiently.
Empirical tests show improved performance on real-world traces.
Abstract
We consider the fundamental problem of designing a self-adjusting tree, which efficiently and locally adapts itself towards the demand it serves (namely accesses to the items stored by the tree nodes), striking a balance between the benefits of such adjustments (enabling faster access) and their costs (reconfigurations). This problem finds applications, among others, in the context of emerging demand-aware and reconfigurable datacenter networks and features connections to self-adjusting data structures. Our main contribution is SeedTree, a dynamically optimal self-adjusting tree which supports local (i.e., greedy) routing, which is particularly attractive under highly dynamic demands. SeedTree relies on an innovative approach which defines a set of unique paths based on randomized item addresses, and uses a small constant number of items per node. We complement our analytical results by…
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Taxonomy
TopicsSoftware-Defined Networks and 5G · Caching and Content Delivery · Interconnection Networks and Systems
