Abstract
Emerging networked systems become increasingly flexible, reconfigurable, and “self-\(*\)”. This introduces an opportunity to adjust networked systems in a demand-aware manner, leveraging spatial and temporal locality in the workload for online optimizations. However, it also introduces a tradeoff: while more frequent adjustments can improve performance, they also entail higher reconfiguration costs. This paper initiates the formal study of list networks which self-adjust to the demand in an online manner, striking a balance between the benefits and costs of reconfigurations. We show that the underlying algorithmic problem can be seen as a distributed generalization of the classic dynamic list update problem known from self-adjusting datastructures: in a network, requests can occur between node pairs. This distributed version turns out to be significantly harder than the classical problem it generalizes. Our main results are a \(\varOmega (\log {n})\) lower bound on the competitive ratio, and a (distributed) online algorithm that is \(\mathcal {O}(\log {n})\)-competitive if the communication requests are issued according to a linear order.
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Notes
- 1.
A function f such that \(f(f(x)) = x\) for all x.
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Avin, C., van Duijn, I., Schmid, S. (2019). Self-adjusting Linear Networks. In: Ghaffari, M., Nesterenko, M., Tixeuil, S., Tucci, S., Yamauchi, Y. (eds) Stabilization, Safety, and Security of Distributed Systems. SSS 2019. Lecture Notes in Computer Science(), vol 11914. Springer, Cham. https://doi.org/10.1007/978-3-030-34992-9_29
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