This book addresses the need to improve TCP's performance inside data centers by providing solutions that are both practical and backward compatible with standard TCP versions. The authors approach this challenge first by deriving an analytical model for TCP's performance under typical data center workload traffic. They then discuss some solutions that are designed to improve TCP performance by either proactively detecting network congestion through probabilistic retransmission or by avoiding timeout penalty through dynamic resizing of TCP segments. Experimental results show that each of techniques discussed outperforms standard TCP inside a data center.

This book provides the first comprehensive study of the applications of stochastic geometry methods in cognitive radio networks. It elaborates the necessary mathematical tools and discusses the state of the art in geometrical modeling and analysis of cognitive radio networks and will be a first general book for the researchers in this field. Readers will be introduced to tools for managing the inherent uncertainty in the spatial locations of wireless nodes in cognitive radio networks, in order to satisfy the interference constraints on the primary receivers and to make the coexistence of primary and secondary networks possible.