Network-aware end-to-end data throughput optimization
Network-aware end-to-end data throughput optimization
复制标题
网络感知的端到端数据吞吐量优化
DOI：
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发表时间：
2011
期刊：
影响因子：
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通讯作者：
T. Kosar
中科院分区：
文献类型：
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作者：
E. Yildirim;T. Kosar
快速发展的光网络技术使我们能够实现高达100 Gbps的高带宽连接。然而，由于低效的传输协议和其他终端系统瓶颈（如磁盘I/O限制、处理器速度和NIC限制），最终用户及其应用程序只能观察到该可用带宽容量的一小部分。在本文中，我们提出了一种新的网络感知的端到端吞吐量预测和优化框架，为我们提供了最佳的参数组合（即并行流，磁盘和CPU数量），以实现最高的端到端吞吐量之间的两个端系统（即集群，数据中心，并行磁盘系统）可能的。我们的实验表明，我们已经开发的模型和算法，使我们能够实现接近最佳的端到端的吞吐量性能，可以忽略不计的采样和预测开销。
The rapidly advancing optical networking technology allows us high-bandwidth connectivity up to 100Gbps these days. However, the end-users and their applications can only observe a fraction of this available bandwidth capacity due to inefficient transport protocols and other end-system bottlenecks such as disk I/O limitations, processor speed, and NIC restrictions. In this paper, we present a novel network-aware end-to-end throughput prediction and optimization framework which provides us with the best parameter combination (i.e. parallel stream, disk, and CPU numbers) to achieve the highest end-to-end throughput between two end-systems (i.e. clusters, data centers, parallel disk systems) possible. Our experiments show that the model and algorithm we have developed enable us to achieve close-to-optimal end-to-end throughput performance with negligible sampling and prediction overhead.