Optimal broadcasting in networks with faulty nodes
September 14, 2017 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Yoel Grinshpon, Ori Gurel-Gurevich
arXiv ID
1709.04885
Category
cs.DS: Data Structures & Algorithms
Cross-listed
cs.DC
Citations
0
Venue
arXiv.org
Last Checked
5 months ago
Abstract
Large computer networks are an essential part of modern technology, and quite often information needs to be broadcast to all the computers in the network. If all computers work perfectly all the time, this is simple. Suppose, however, that some of the computers fail occasionally. What is the fastest way to ensure that with high probability all working computers get the information? In this paper, we analyze three algorithms to do so. All algorithms terminate in logarithmic time, assuming computers fail with probability $1-p$ independently of each other. We prove that the third algorithm, which runs in time $(1+o(1))(\frac{\log N}{\log(1+p)})$, is asymptotically optimal.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Data Structures & Algorithms
π
π
The Cartographer
R.I.P.
π»
Ghosted
Route Planning in Transportation Networks
R.I.P.
π»
Ghosted
Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration
R.I.P.
π»
Ghosted
Hierarchical Clustering: Objective Functions and Algorithms
R.I.P.
π»
Ghosted
Graph Isomorphism in Quasipolynomial Time
π
π
The Cartographer
Simulation optimization: A review of algorithms and applications
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted