Paper Info
Title
Faster information dissemination in dynamic networks via network coding
Abstract
We use network coding to improve the speed of distributed computation in the dynamic network model of Kuhn, Lynch and Oshman [STOC '10]. In this model an adversary adaptively chooses a new network topology in every round, making even basic distributed computations challenging. Kuhn et al. show that n nodes, each starting with a d-bit token, can broadcast them to all nodes in time O(n2) using b-bit messages, where b d + log n. Their algorithms take the natural approach of token forwarding: in every round each node broadcasts some particular token it knows. They prove matching Ω(n2) lower bounds for a natural class of token forwarding algorithms and an Ω(n log n) lower bound that applies to all token-forwarding algorithms. We use network coding, transmitting random linear combinations of tokens, to break both lower bounds. Our algorithm's performance is quadratic in the message size b, broadcasting the n tokens in roughly d/b2 * n2 rounds. For b = d = Θ(log n) our algorithms use O(n2/log n) rounds, breaking the first lower bound, while for larger message sizes we obtain linear-time algorithms. We also consider networks that change only every T rounds, and achieve an additional factor T2 speedup. This contrasts with related lower and upper bounds of Kuhn et al. implying that for natural token-forwarding algorithms a speedup of T, but not more, can be obtained. Lastly, we give a general way to derandomize random linear network coding, that also leads to new deterministic information dissemination algorithms.
Year
DOI
Venue
2011
10.1145/1993806.1993885
principles of distributed computing
Keywords
DocType
Volume
lower bound,data structure,distributed computing,network coding,cluster computing,network topology
Conference
abs/1104.2527
Citations 
PageRank 
References 
13
0.53
12
Authors
2
Name
Order
Citations
PageRank
Bernhard Haeupler162854.00
David R. Karger2193672233.64