Pourdamghani, ArashArashPourdamghaniAvin, ChenChenAvinSama, RobertRobertSamaSchmid, StefanStefanSchmid2024-02-292024-02-292023https://publica.fraunhofer.de/handle/publica/46268310.48550/arXiv.2301.03074We 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 showing the benefits of SeedTree empirically, evaluating it on various synthetic and real-world communication traces.enSelf-adjusting data structuresreconfigurable datacentersonline algorithmspaper