TY - GEN
T1 - Self-adjusting linear networks
AU - Avin, Chen
AU - van Duijn, Ingo
AU - Schmid, Stefan
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2019.
PY - 2019/1/1
Y1 - 2019/1/1
N2 - Emerging networked systems become increasingly flexible and “reconfigurable”. 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 linear 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 in generalizes. Our main results are a Ω(log n) lower bound on the competitive ratio, and a (distributed) online algorithm that is O(log n)- -competitive if the communication requests are issued according to a linear order.
AB - Emerging networked systems become increasingly flexible and “reconfigurable”. 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 linear 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 in generalizes. Our main results are a Ω(log n) lower bound on the competitive ratio, and a (distributed) online algorithm that is O(log n)- -competitive if the communication requests are issued according to a linear order.
KW - Communication networks
KW - Competitive analysis
KW - Distributed algorithms
KW - Self-adjusting datastructures
UR - http://www.scopus.com/inward/record.url?scp=85069839599&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-24922-9_23
DO - 10.1007/978-3-030-24922-9_23
M3 - Conference contribution
AN - SCOPUS:85069839599
SN - 9783030249212
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 332
EP - 335
BT - Structural Information and Communication Complexity - 26th International Colloquium, SIROCCO 2019, Proceedings
A2 - Censor-Hillel, Keren
A2 - Flammini, Michele
PB - Springer Verlag
T2 - 26th International Colloquium on Structural Information and Communication Complexity, SIROCCO 2019
Y2 - 1 July 2019 through 4 July 2019
ER -