Optimized HybridSketch: More Efficient with Analysis and Algorithm

Abstract

The sketch structure is widely applied in the network measurement field due to its limited memory usage and simple operation. However, When the less memory space the system occupied, the accuracy decreases. However, as the flow rate rapidly increases, the on-chip memory will become a system bottleneck. HybridSketch provides a methods to save memory and maintain the accuracy of the measurement system. We apply analysis and new algorithms to make it more efficient. We analyze the error bound of the system and observed that the sketch part of the system will lose the precision of the information along with less memory inevitably. So we propose the data augmentation algorithm based on our analysis. We apply it and propose the optimized HybridSketch. We evaluate the performance and present a comparison with the origin algorithm. The results show that optimized HybridSketch provides an 80% precision rate compared to the original one which occupied 10× the memory size.