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UID:calendar.24939.field_data.0@oba.diag.uniroma1.it
DTSTAMP:20260405T060144Z
CREATED:20221102T213313Z
DESCRIPTION:Monitoring large data streams and maintaining statistics about 
 them is a challenging task\, revolving around the tradeoff triangle betwee
 n memory frugality\, computational complexity\, and accuracy. The two comm
 on approaches for addressing these problems are sketching and sampling. In
  this talk\, I will present a couple of examples of how an effective combi
 nation of the two can yield better results than either of them.The first e
 xample is NitroSketch\, a generic framework that boosts the performance of
  all sketches that employ multiple counter arrays\, including\, e.g.\, the
  famous count-min sketch\, count-sketch\, and Univmon. NitroSketch systema
 tically addresses the performance bottlenecks of sketches without sacrific
 ing robustness and generality. Its key contribution is the careful synthes
 is of rigorous\, yet practical solutions to reduce the number of per-packe
 t CPU and memory operations. NitroSketch is implemented on three popular s
 oftware switch platforms (Open vSwitch-DPDK\, FD.io-VPP\, and BESS). Our p
 erformance evaluation shows that accuracy is comparable to unmodified sket
 ches while attaining up to two orders ofmagnitude speedup\, and up to 45% 
 reduction in CPU usage.The second example is SQUAD\, a novel algorithm for
  tracking quantiles (e.g.\, tail latencies) of significant items within a 
 stream\, where an item can be the source IP + destination IP addresses in 
 a networking application\, a URI or a user ID in a web service\, or an obj
 ect ID in a key-value store. While quantile sketches have been studied in 
 the past\, naively applying one instance of such sketches to each item is 
 very memory wasteful. Similarly\, applying sampling alone also requires pr
 ohibitive amounts of memory. In contrast\, SQUAD addresses this problem by
  combining sampling and sketching in a way that improves the asymptotic sp
 ace complexity. Intuitively\, SQUAD allocates a sketch only to items ident
 ified as likely to be significant and uses a background sampling process t
 o capture the behavior of the quantiles of an item before it is allocated 
 with a sketch. This allows SQUAD to use fewer samples and sketches. An emp
 irical evaluation demonstrates SQUAD’s superiority using extensive simulat
 ions on real-world traces.* Based on joint works with Ran Ben-Basat\, Vlad
 imir Braverman\, Gil Einziger\, Yaron Kassner\, Zaoxing Liu\, Vyas Sekar\,
  and Rana Shahout
DTSTART;TZID=Europe/Paris:20221117T110000
DTEND;TZID=Europe/Paris:20221117T110000
LAST-MODIFIED:20230710T173816Z
LOCATION:Aula Magna DIAG
SUMMARY:Better Together: Combining Sketching and Sampling for Effective Str
 eam Processing - Roy Friedman
URL;TYPE=URI:http://oba.diag.uniroma1.it/node/24939
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