Survivability in Time-varying Networks
December 28, 2015 ยท Declared Dead ยท ๐ IEEE INFOCOM 2016 - The 35th Annual IEEE International Conference on Computer Communications
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Qingkai Liang, Eytan Modiano
arXiv ID
1512.08299
Category
cs.NI: Networking & Internet
Citations
31
Venue
IEEE INFOCOM 2016 - The 35th Annual IEEE International Conference on Computer Communications
Last Checked
3 months ago
Abstract
Time-varying graphs are a useful model for networks with dynamic connectivity such as vehicular networks, yet, despite their great modeling power, many important features of time-varying graphs are still poorly understood. In this paper, we study the survivability properties of time-varying networks against unpredictable interruptions. We first show that the traditional definition of survivability is not effective in time-varying networks, and propose a new survivability framework. To evaluate the survivability of time-varying networks under the new framework, we propose two metrics that are analogous to MaxFlow and MinCut in static networks. We show that some fundamental survivability-related results such as Menger's Theorem only conditionally hold in time-varying networks. Then we analyze the complexity of computing the proposed metrics and develop several approximation algorithms. Finally, we conduct trace-driven simulations to demonstrate the application of our survivability framework to the robust design of a real-world bus communication network.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
๐ Similar Papers
In the same crypt โ Networking & Internet
R.I.P.
๐ป
Ghosted
R.I.P.
๐ป
Ghosted
Federated Learning in Mobile Edge Networks: A Comprehensive Survey
R.I.P.
๐ป
Ghosted
A Survey of Indoor Localization Systems and Technologies
R.I.P.
๐ป
Ghosted
Survey of Important Issues in UAV Communication Networks
R.I.P.
๐ป
Ghosted
Network Function Virtualization: State-of-the-art and Research Challenges
R.I.P.
๐ป
Ghosted
Applications of Deep Reinforcement Learning in Communications and Networking: A Survey
Died the same way โ ๐ป Ghosted
R.I.P.
๐ป
Ghosted
Language Models are Few-Shot Learners
R.I.P.
๐ป
Ghosted
PyTorch: An Imperative Style, High-Performance Deep Learning Library
R.I.P.
๐ป
Ghosted
XGBoost: A Scalable Tree Boosting System
R.I.P.
๐ป
Ghosted