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Enhancing Network Performance Tomography in Software-Defined Cloud Network
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DOI：10.1109/LCOMM.2016.2640293.png)
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Abstract
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.
Keywords:
Tomography
Monitoring
Mathematical model
Cloud computing
Packet loss
Delays
Computer networks
Software defined networking
Journal
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