With Great Speed Come Small Buffers: Space-Bandwidth Tradeoffs for Routing
February 21, 2019 Β· Declared Dead Β· π ACM SIGACT-SIGOPS Symposium on Principles of Distributed Computing
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Avery Miller, Boaz Patt-Shamir, Will Rosenbaum
arXiv ID
1902.08069
Category
cs.DS: Data Structures & Algorithms
Cross-listed
cs.DC
Citations
5
Venue
ACM SIGACT-SIGOPS Symposium on Principles of Distributed Computing
Last Checked
4 months ago
Abstract
We consider the Adversarial Queuing Theory (AQT) model, where packet arrivals are subject to a maximum average rate $0\leΟ\le1$ and burstiness $Ο\ge0$. In this model, we analyze the size of buffers required to avoid overflows in the basic case of a path. Our main results characterize the space required by the average rate and the number of distinct destinations: we show that $O(k d^{1/k})$ space suffice, where $d$ is the number of distinct destinations and $k=\lfloor 1/Ο\rfloor$; and we show that $Ξ©(\frac 1 k d^{1/k})$ space is necessary. For directed trees, we describe an algorithm whose buffer space requirement is at most $1 + d' + Ο$ where $d'$ is the maximum number of destinations on any root-leaf path.
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