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Finding heavy hitters has been of vital importance in network measurement. Among all the recent works in finding heavy hitters, the Elastic sketch achieves the highest accuracy and fastest speed. However, we find that there is still room for improvement of the Elastic sketch in finding heavy hitters. In this paper, we propose a tailored Elastic to enhance the sketch only for finding heavy hitters at the cost of losing the generality of Elastic. To tailor Elastic, we abandon the light part, and improve the eviction strategy. Our experimental results show that compared with the standard Elastic, our tailored Elastic reduces the error rate to 5.7~8.1 times and increases the speed to 2.5 times. All the related source codes and datasets are available at Github.
We study the heavy hitters and related sparse recovery problems in the low-failure probability regime. This regime is not well-understood, and has only been studied for non-adaptive schemes. The main previous work is one on sparse recovery by Gilbert
We study the distinct elements and $ell_p$-heavy hitters problems in the sliding window model, where only the most recent $n$ elements in the data stream form the underlying set. We first introduce the composable histogram, a simple twist on the expo
Network device syslogs are ubiquitous and abundant in modern data centers with most large data centers producing millions of messages per day. Yet, the operational information reflected in syslogs and their implications on diagnosis or management tas
Wireless Sensor Networks research and demand are now in full expansion, since people came to understand these are the key to a large number of issues in industry, commerce, home automation, healthcare, agriculture and environment, monitoring, public
Elastic optical network (EON) efficiently utilize spectral resources for optical fiber communication by allocating the minimum necessary bandwidth to client demands. On the other hand, network traffic has been continuously increasing due to the wide