A population dynamics model for data streaming over P2P networks

Data streaming (DS) over Peer-to-Peer (P2P) networks has been intensively studied in recent years and there have been various schemes proposed already. To evaluate these schemes, either measurement in experimental implementations, or simulation and theoretical analysis have been used. The former is inadequate as data are collected from different experiments, while the latter lacks a proper theoretical dynamics model. Our research aims at providing a general theoretical model to evaluate DS over P2P systems and analyze their dynamic behaviors. In this paper, with the analysis and abstraction of the characteristics of peers and their organization in DS over P2P, we propose a general population dynamics model for DS over P2P with fixed population. The model depicts the dynamic distribution of peers as a closed Markov queuing network. In particular, the model is scheme-independent and can be used with various schemes. Through theoretical analysis, we prove the model has equilibrium and only one closed-form solution. Besides, we verify the model through simulations, and show that it is a helpful analytical tool with a case study. © 2009 IEEE.published_or_final_versio

A Comprehensive Analysis of Swarming-based Live Streaming to Leverage Client Heterogeneity

Due to missing IP multicast support on an Internet scale, over-the-top media streams are delivered with the help of overlays as used by content delivery networks and their peer-to-peer (P2P) extensions. In this context, mesh/pull-based swarming plays an important role either as pure streaming approach or in combination with tree/push mechanisms. However, the impact of realistic client populations with heterogeneous resources is not yet fully understood. In this technical report, we contribute to closing this gap by mathematically analysing the most basic scheduling mechanisms latest deadline first (LDF) and earliest deadline first (EDF) in a continuous time Markov chain framework and combining them into a simple, yet powerful, mixed strategy to leverage inherent differences in client resources. The main contributions are twofold: (1) a mathematical framework for swarming on random graphs is proposed with a focus on LDF and EDF strategies in heterogeneous scenarios; (2) a mixed strategy, named SchedMix, is proposed that leverages peer heterogeneity. The proposed strategy, SchedMix is shown to outperform the other two strategies using different abstractions: a mean-field theoretic analysis of buffer probabilities, simulations of a stochastic model on random graphs, and a full-stack implementation of a P2P streaming system.Comment: Technical report and supplementary material to http://ieeexplore.ieee.org/document/7497234

Modeling and Evaluation of Multisource Streaming Strategies in P2P VoD Systems

In recent years, multimedia content distribution has largely been moved to the Internet, inducing broadcasters, operators and service providers to upgrade with large expenses their infrastructures. In this context, streaming solutions that rely on user devices such as set-top boxes (STBs) to offload dedicated streaming servers are particularly appropriate. In these systems, contents are usually replicated and scattered over the network established by STBs placed at users' home, and the video-on-demand (VoD) service is provisioned through streaming sessions established among neighboring STBs following a Peer-to-Peer fashion. Up to now the majority of research works have focused on the design and optimization of content replicas mechanisms to minimize server costs. The optimization of replicas mechanisms has been typically performed either considering very crude system performance indicators or analyzing asymptotic behavior. In this work, instead, we propose an analytical model that complements previous works providing fairly accurate predictions of system performance (i.e., blocking probability). Our model turns out to be a highly scalable, flexible, and extensible tool that may be helpful both for designers and developers to efficiently predict the effect of system design choices in large scale STB-VoD system

QoE in Pull Based P2P-TV Systems: Overlay Topology Design Tradeoff

Abstract—This paper presents a systematic performance anal-ysis of pull P2P video streaming systems for live applications, providing guidelines for the design of the overlay topology and the chunk scheduling algorithm. The contribution of the paper is threefold: 1) we propose a realistic simulative model of the system that represents the effects of access bandwidth heterogeneity, latencies, peculiar characteristics of the video, while still guaranteeing good scalability properties; 2) we propose a new latency/bandwidth-aware overlay topology design strategy that improves application layer performance while reducing the underlying transport network stress; 3) we investigate the impact of chunk scheduling algorithms that explicitly exploit properties of encoded video. Results show that our proposal jointly improves the actual Quality of Experience of users and reduces the cost the transport network has to support. I

The state of peer-to-peer network simulators

Networking research often relies on simulation in order to test and evaluate new ideas. An important requirement of this process is that results must be reproducible so that other researchers can replicate, validate and extend existing work. We look at the landscape of simulators for research in peer-to-peer (P2P) networks by conducting a survey of a combined total of over 280 papers from before and after 2007 (the year of the last survey in this area), and comment on the large quantity of research using bespoke, closed-source simulators. We propose a set of criteria that P2P simulators should meet, and poll the P2P research community for their agreement. We aim to drive the community towards performing their experiments on simulators that allow for others to validate their results

Chemical Reaction Optimization for population transition in peer-to-peer live streaming

Peer-to-peer (P2P) live streaming applications are very popular in recent years and a Markov open queueing network model was developed to study the population dynamics in P2P live streaming. Based on the model, we deduce an optimization problem, called population transition problem, with the objective of maximizing the probability of universal streaming by manipulating population transition probability matrix. We employ a chemical reaction-inspired metaheuristic, Chemical Reaction Optimization (CRO), to solve the problem. Simulation results show that CRO outperforms many commonly used strategies for controlling population transition in many practical P2P live streaming systems. This work also shows that CRO also demonstrates the usability of CRO to solve optimization problems. © 2010 IEEE.published_or_final_versionThe IEEE Congress on Evolutionary Computation (CEC), Barcelona, Spain, 18-23 July 2010. In Proceedings of the IEEE CEC, 2010, p. 1-