FullSight: Towards Scalable, High-Coverage, and Fine-grained Network Telemetry
Sen Ling, Waixi Liu, Yinghao Zhu, Miaoquan Tan, Jieming Huang, Zhenzheng Guo, Wen-Hong Lin · 2021 17th International Conference on Mobility, Sensing and Networking (MSN) · 2021
A variety of network states can better help network operators to manage the whole network. However, the existing network measurement schemes still exhibit some drawbacks, such as excessive bandwidth overhead caused by running packet-level measurement, lack of coverage of variety measurement granularities, and occupying several switch’s memory. This paper presents the FullSight based on the programmable data plane, which provides fine multiple granularities measurement. Based on the programmability of data plane, this paper proposes an intelligent measurement mechanism that can adaptively adjust the measurement frequency according to the network state to greatly reduce the bandwidth overhead of measuring while ensuring a certain measurement accuracy and acceptable processing overhead. Also, the Rotating Memory scheme is proposed to reduce occupying memory of switch when achieving a variety of fine-grained measurements. The simulation results demonstrate the effectiveness of FullSight in terms of the bandwidth overhead reduction, the memory overhead reduction, full coverage of a variety of fine-grained network states. Compared with Netsight, FullSight only suffers from 0. 1% bandwidth overhead which is two orders of magnitude lower than Netsight, and FullSight has taken up no more than 0. 001% memory overhead for different measurement tasks.