  Cloud operators require real-time identification of Heavy Hitters (HH) and
Hierarchical Heavy Hitters (HHH) for applications such as load balancing,
traffic engineering, and attack mitigation. However, existing techniques are
slow in detecting new heavy hitters.
  In this paper, we make the case for identifying heavy hitters through
\textit{sliding windows}. Sliding windows detect heavy hitters quicker and more
accurately than current methods, but to date had no practical algorithms.
Accordingly, we introduce, design and analyze the \textit{Memento} family of
sliding window algorithms for the HH and HHH problems in the single-device and
network-wide settings. Using extensive evaluations, we show that our
single-device solutions attain similar accuracy and are by up to $273\times$
faster than existing window-based techniques. Furthermore, we exemplify our
network-wide HHH detection capabilities on a realistic testbed. To that end, we
implemented Memento as an open-source extension to the popular HAProxy cloud
load-balancer. In our evaluations, using an HTTP flood by 50 subnets, our
network-wide approach detected the new subnets faster, and reduced the number
of undetected flood requests by up to $37\times$ compared to the alternatives.
