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Transmission Scheduling in Data-Driven Peer-to-Peer Streaming towards Optimal Throughput

机译:数据驱动对等体流中的传输调度朝向最佳吞吐量

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Peer-to-peer media streaming has been an important service on the internet in recent years. The Data-driven (or mesh-based) structure is adopted by most working systems,in which data scheduling is one of the important problems.However, those frequently used scheduling algorithms are often faced with such a case: A neighbor peer takes up its bandwidth to deliver the packets that other neighbors can also supply, but some packets only held by it are not delivered.These packets can not be delivered in the current scheduling cycle, even though that the other neighbors have surplus bandwidth. This is a kind of waste of bandwidth and decreases the throughput of transmission. In this paper we propose anew scheduling algorithm aiming at the optimal throughput:Bipartite-matching based Block Scheduling algorithm(BBS).We convert the original data scheduling problem to a problem of finding a maximum match on the correspond bipartite graph, then assign data packets to neighbors according to the maximum match. We evaluate the performance of BBS with extensive experiments and the results show that BBS throughput and provides better streaming quality than those frequently used scheduling algorithms.
机译:近年来,点对点媒体流媒体是互联网上的重要服务。大多数工作系统采用数据驱动(或基于网格的)结构,其中数据调度是重要问题之一。然而,常用的调度算法通常面临这样的情况:邻居对等体占据其带宽来传递其他邻居也可以提供的数据包,但仅由其保持的一些数据包未被传递。这些数据包不能在当前调度周期中传递,即使其他邻居具有剩余带宽。这是一种带宽的浪费,降低了传输的吞吐量。在本文中,我们提出了一种针对最佳吞吐量的重新调度算法:基于最优吞吐量的调度算法:基于二分匹配的块调度算法(BBS).we将原始数据调度问题转换为在对应的二分钟上找到最大匹配的问题,然后分配数据包根据最大匹配的邻居。我们评估BBS具有广泛实验的BBS性能,结果表明BBS吞吐量并提供比经常使用的调度算法更好的流质量。

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