On Resource Aware Algorithms in Epidemic Live Streaming

TL;DR

This paper investigates resource-aware peer selection algorithms in epidemic live streaming, analyzing their performance in heterogeneous networks through simulations and highlighting the importance of early diffusion and fairness trade-offs.

Contribution

It introduces a generic model for peer selection combining aware and agnostic functions, and analyzes their impact in non-homogeneous systems.

Findings

Early chunk diffusion is crucial for overall performance.

Awareness level affects fairness among heterogeneous peers.

Source distribution policy impacts diffusion efficiency.

Abstract

Epidemic-style diffusion schemes have been previously proposed for achieving peer-to-peer live streaming. Their performance trade-offs have been deeply analyzed for homogeneous systems, where all peers have the same upload capacity. However, epidemic schemes designed for heterogeneous systems have not been completely understood yet. In this report we focus on the peer selection process and propose a generic model that encompasses a large class of algorithms. The process is modeled as a combination of two functions, an aware one and an agnostic one. By means of simulations, we analyze the awareness-agnostism trade-offs on the peer selection process and the impact of the source distribution policy in non-homogeneous networks. We highlight that the early diffusion of a given chunk is crucial for its overall diffusion performance, and a fairness trade-off arises between the performance of heterogeneous peers, as a function of the level of awareness.