Stochastic Control of Multi-Scale Networks: Modeling, Analysis and Algorithms
Abstract
Our investigation consists of three inter-related thrusts: traffic modeling and analysis, network control, and information assurance in wireline and wireless networks. We model multi-scale behavior in network systems, where traffic and system behavior can be highly correlated over multiple time scales (e.g., LRD). We investigate the causes of LRD traffic in network systems, which may result from traffic correlation, protocol behavior (e.g., retransmissions), and network congestion; and statistically analyzed the properties of LRD traffic from empirical data sets. We develop a unifying theory for network control that exploits the interactions across network functionalities, operates at appropriate time-scales, and is effective in the presence of LRD. We formulate optimization and distributed control problems for providing network services, and study the impact of LRD traffic on network control, performance, and security. We also develop an integrative approach that combines the LRD modeling and network control to obtain non-parametric or semi-parametric techniques for the distributed detection of information flow and flow changes needed for preventing security attacks. We characterize flow detectability as a function of flow rate, delay and memory constraints, and developed distributed detection schemes that guarantee vanishingly low detection error probabilities.
Document Details
- Document Type
- Technical Report
- Publication Date
- Oct 20, 2014
- Accession Number
- ADA616834
Entities
People
- Eytan Modiano
- Ian F. Akyildiz
- Lang Tong
- Ness Shroff
- P. R. Kumar
- Swapna Buccapatnam
Organizations
- Ohio State University