基于智能网络交换机数据流管理的网络流量动态优化算法

Souryendu Das, Wei Lian, Stavros Kalafatis, P. Lazaridis
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引用次数: 1

摘要

网络影响着我们与数字世界的日常互动。智能设备技术的进步导致了对更快、更节能和无差错网络的需求。网络协议,如TCP和UDP,最初设计时没有这些新要求,现在面临着适应这些目标的重大挑战,即有效处理交换网络中的拥塞延迟,增加生产成本和更高的数据包丢失。在本文中,我们提出了智能交换机动态延迟算法(SSDDA),该算法考虑了TCP/UDP链路的链路利用率、应用层吞吐量(goodput)、丢包和排队延迟等多种因素。应用于数据中心交换机,其中交换可以从集中式控制器转移到分散的数据路径和可编程智能交换机。来自网络的持续反馈使SSDDA能够估计延迟并修改数据包流以获得最佳性能。在胖树拓扑中使用NS3进行了仿真,验证了SSDDA算法的优点。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Dynamic Algorithm for Optimization of Network Traffic through Smart Network Switch Data Flow Management
Networking affects our daily interaction with the digital world. Advancements in Smart Device technology have led to the need for faster, more power-efficient, and error-free networks. Networking protocols, such as TCP and UDP, were originally designed without these new requirements and now face significant challenges in adapting to those goals i.e., efficiently handling congestion delays in switching networks, increasing production costs, and higher packet losses. In this paper, we propose the Smart Switch Dynamic Delay Algorithm (SSDDA) which takes into consideration multiple factors including link utilization, application-level throughput (goodput), packet loss, and queuing delay of the TCP/UDP link. The application is in datacenter switches where the switching could be moved from centralized controller to decentralized data path and programmable smart switches. Continuous feedback from the network enables SSDDA to estimate delay and modify packet flows for optimal performance. Simulation results using NS3 in a fat tree topology are carried out validating the algorithmic benefits of SSDDA.
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