Enhanced Machine Learning Sketches for Network Measurements

—Network monitoring and management require accurate statistics of a variety of ﬂow-level metrics such as ﬂow sizes, top- k ﬂows, and number of ﬂows. Arguably, the current best technique to measure these metrics is sketches. While a signiﬁcant amount of work has already been done on sketching techniques, there is still a lot of room for improvement because the accuracy of existing sketches varies with changing characteristics of network trafﬁc. In this paper, we propose the idea of using machine learning to improve the accuracy of sketches, and propose a generic machine learning framework to reduce the dependence of accuracy of sketches on network trafﬁc characteristics. We further present three case studies, where we applied our machine learning framework on sketches for measuring three ﬂow-level network metrics, namely ﬂow sizes, top- k ﬂows, and number of ﬂows. We implemented and extensively evaluated this framework for these three metrics using both real-world and synthetic trafﬁc traces. To the best of our knowledge, this is the ﬁrst work that uses machine learning to reduce the dependence of sketching techniques on the characteristics of network trafﬁc. We have released all our traces and implementation codes at Github.