TY - UNPB
T1 - ReNets
T2 - Toward Statically Optimal Self-Adjusting Networks.
AU - Avin, Chen
AU - Schmid, Stefan
N1 - DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.
PY - 2019/4/5
Y1 - 2019/4/5
N2 - This paper studies the design of self-adjusting networks whose topology dynamically adapts to the workload, in an online and demand-aware manner. This problem is motivated by emerging optical technologies which allow to reconfigure the datacenter topology at runtime. Our main contribution is ReNet, a self-adjusting network which maintains a balance between the benefits and costs of reconfigurations. In particular, we show that ReNets are statically optimal for arbitrary sparse communication demands, i.e., perform at least as good as any fixed demand-aware network designed with a perfect knowledge of the futuredemand. Furthermore, ReNets provide compact and local routing, by leveraging ideas from self-adjusting datastructures.
AB - This paper studies the design of self-adjusting networks whose topology dynamically adapts to the workload, in an online and demand-aware manner. This problem is motivated by emerging optical technologies which allow to reconfigure the datacenter topology at runtime. Our main contribution is ReNet, a self-adjusting network which maintains a balance between the benefits and costs of reconfigurations. In particular, we show that ReNets are statically optimal for arbitrary sparse communication demands, i.e., perform at least as good as any fixed demand-aware network designed with a perfect knowledge of the futuredemand. Furthermore, ReNets provide compact and local routing, by leveraging ideas from self-adjusting datastructures.
M3 - Preprint
BT - ReNets
ER -