גיל אינציגר

The q-MAX problem, which seeks to find the q largest elements in a data stream, has numerous networking applications including sketches, network-wide heavy hitters, and others. In this poster, we propose an improvement to the q-MAX algorithm [5] that leverages sampling to accelerate the computation. Despite being randomized, our algorithm never fails (i.e., it is a Las Vegas algorithm) and runs up to 62% faster when evaluated on real packet traces and tasks. Moreover, on a real networking application and workload, our algorithm provides an 11-53% higher throughput.