首页> 外文期刊>Mobile Computing, IEEE Transactions on >Concurrent Multipath Transfer Using SCTP: Modelling and Congestion Window Management
【24h】

Concurrent Multipath Transfer Using SCTP: Modelling and Congestion Window Management

机译:使用SCTP的并发多路径传输:建模和拥塞窗口管理

获取原文
获取原文并翻译 | 示例

摘要

Concurrent multipath transfer (CMT) using the stream control transmission protocol (SCTP) can exploit multihomed devices to enhance data communications. While SCTP is a new transport layer protocol supporting multihomed end-points, CMT provides a framework so that transport layer resources are used efficiently and effectively when sending to the same destination with multiple IP addresses. In this paper, we present two techniques for modelling the expected throughput of a CMT session; while one is based on renewal theory, the other uses a Markov chain. As far as we know, ours is the first paper to model CMT whilst considering practical transport layer resources like a shared receive buffer (RBUF). A comparison of the models showed the Markov chain to be more accurate, but suffered from scalability issues. Alternatively, the renewal model was more cost effective, but also less accurate. We also applied our models to a new problem called congestion window management, where the size of each congestion window is reconfigured for optimal performance. Again, we compared two approaches: a dynamic method that makes decisions based on instantaneous throughput, and a static method that uses an integer linear program (ILP) to generate a global solution. Results showed the static method outperforming the dynamic approach by as much as 12 percent.
机译:使用流控制传输协议(SCTP)的并发多路径传输(CMT)可以利用多宿主设备来增强数据通信。 SCTP是支持多宿主端点的新传输层协议,而CMT提供了一个框架,以便在发送到具有多个IP地址的相同目的地时,可以高效地使用传输层资源。在本文中,我们提出了两种对CMT会话的预期吞吐量进行建模的技术。一种基于更新理论,另一种则使用马尔可夫链。据我们所知,我们是第一篇对CMT建模的论文,同时考虑了诸如共享接收缓冲区(RBUF)之类的实际传输层资源。对模型的比较表明,马尔可夫链更为精确,但存在可伸缩性问题。另外,更新模型更具成本效益,但准确性也较低。我们还将模型应用于一个称为拥塞窗口管理的新问题,该问题中每个拥塞窗口的大小都已重新配置以实现最佳性能。再次,我们比较了两种方法:一种基于瞬时吞吐量进行决策的动态方法,以及一种使用整数线性程序(ILP)生成全局解决方案的静态方法。结果表明,静态方法比动态方法要好12%。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号