.png){kind=icon}
返回
Enhancing Network Performance Tomography in Software-Defined Cloud Network
.png){kind=icon}
PRE
AI
DOI：10.1109/LCOMM.2016.2640293.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
For cloud network performance profiling, network tomography based on end-to-end measurement is often used in deducing the network performance for its efficiency. However, most tomography problems are under-constrained, which require additional assumptions or probing monitors planted among network switches, which are often unavailable in software-defined networking (SDN) environment. On the other hand, SDN-based flow mirroring could provide accurate flow information, but the cost of both gathering and analysing the packet traces is tremendous that it is impossible to cover the whole network. We propose ScoutFlow, a method combining SDN flow measurement and end-to-end performance tomography, to achieve accurate performance profiling for cloud network while keeping low monitoring overhead. We evaluate ScoutFlow in our campus data center cloud, and the experiment shows good scalability and accuracy.
Keyword:
Tomography
Monitoring
Mathematical model
Cloud computing
Packet loss
Delays
Computer networks
Software defined networking
期刊
IF:
4.4
论文数:
1.3W
被引数:
2.2W
机构
引用论文
.png){kind=icon}
分享收藏
查看更多内容