HybridSketch: A Memory-centric Precise Approach for Flow Measurement

As network bandwidth has rapidly developed, due to the high occupancy of memory and bandwidth required, the Sketch structure is favored by some researchers due to its limited memory usage and simple operation. But the accuracy will decrease when the Sketch system occupies less memory space. Traditional sketch algorithms and some other specially designed algorithms and structures are striving to improve accuracy. However, with the flow rate rapidly increasing, the on-chip memory will be the bottleneck of the system. Our network measurement system achieve good results focusing more on the memory usage. We proposes a hybrid method, HybridSketch, which focuses on the memory and precision of the system with mixing two measurement methods by quantitatively analyzing, modeling and allocating appropriate memory space to each method to achieve better results. Experimental results show that our method can provide 10x improvement in terms of precision, moreover, HybridSketch can provide the same level of precision with achieving 24x improvement in terms of memory size.