This paper develops analytical models that characterize the behavior of on-demand stored media content delivery using BitTorrent-like protocols. The models capture the effects of different piece selection policies, including Rarest-First, two variants of In-Order, and two probabilistic policies (Portion and Zipf). Our models provide insight into system behavior and help explain the sluggishness of the system with In-Order streaming. We use the models to compare different retrieval policies across a wide range of system parameters, including peer arrival rate, upload/download bandwidth, and seed residence time. We also provide quantitative results on the startup delays and retrieval times for streaming media delivery. Our results provide insights into the design tradeoffs for on-demand media streaming in peer-to-peer networks. Finally, the models are validated using simulations.

This paper develops analytic models that characterize the behavior of on-demand stored media content delivery using BitTorrent-like protocols. The models capture the effects of different piece selection policies, including Rarest-First and two variants of In-Order. Our models provide insight into transient and steady-state system behavior, and help explain the sluggishness of the system with strict In-Order streaming. We use the models to compare different retrieval policies across a wide range of system parameters, including peer arrival rate, upload/download bandwidth, and seed residence time. We also provide quantitative results on the startup delays and retrieval times for streaming media delivery. Our results provide insights into the optimal design of peer-to-peer networks for on-demand media streaming.

In this paper, we compare the throughputs of two different TCP NewReno variants, namely Slow-but-Steady and Impatient. We develop analytic throughput models of these variants as a function of round-trip time, loss event rate, and the burstiness of packet drops within a loss event. Our models build upon prior work on TCP Reno throughput modeling, but extend this work to provide an analytical characterization of the NewReno fast recovery algorithms. We validated our models using the ns-2 simulator. Our models accurately predict the steady-state NewReno throughput for a wide range of loss rates. Based on these models, we analytically determine the preferred operating regions for each TCP variant. Our results show that the Slow-but-Steady variant is comparable to or superior to the Impatient variant in all but the most extreme scenarios for network packet loss.

The paper presents a novel dynamic bandwidth estimation mechanism for improving TCP (Transmission Control Protocol) performance in wired-cum-wireless networks. The key idea is to measure continuously the bandwidth used by a TCP flow by monitoring the rate of returning acknowledgements (ACKs) and the round-trip time (RTT) values. The distinguishing feature of this mechanism (compared to other mechanisms such as that in TCP Westwood) is that it exploits the burstiness pattern of ACK arrivals and estimates the available bandwidth more accurately. In the proposed mechanism, the bandwidth sample is calculated by distributing a burst of ACKs over an off period based on the degree of congestion and burstiness in the network. The estimation technique is robust against burstiness of ACK arrival and type of loss (e.g., wireless loss, congestion loss). A new variant of TCP New-Reno based on this adaptive bandwidth estimation technique is referred to as TCP Prairie. Simulation results obtained using ns-2 reveal that TCP Prairie provides significant throughput performance improvement over TCP New-Reno and TCP Westwood under congestion and/or wireless loss scenarios. Also, compared to TCP Westwood, TCP Prairie is observed to be more friendly towards TCP New-Reno.