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VSketcher v1.0.8 Portable Cracked Download Generalized Sketch Families for Network Traffic
YOU ZHOU∗, University of Florida, USA
YOULIN ZHANG∗, University of Florida, USA
CHAOYI MA, University of Florida, USA
SHIGANG CHEN, University of Florida, USA
OLUFEMI O ODEGBILE, University of Florida, USA
Traffic measurement provides critical information for network management, resource allocation,
traffic engineering, and attack detection. Most prior art has been geared towards specific application
needs with specific performance objectives. To support diverse requirements with efficient and
future-proof implementation, this paper takes a new approach to establish common frameworks,
each for a family of traffic measurement solutions that share the same implementation structure,
providing a high level of generality, for both size and spread measurements and for all flows.
The designs support many options of performance-overhead tradeoff with as few as one memory
update per packet and as little space as several bits per flow on average. Such a family-based
approach will unify implementation by removing redundancy from different measurement tasks and
support reconfigurability in a plug-n-play manner. We demonstrate the connection and difference
in the design of these traffic measurement families and perform experimental comparisons on
hardware/software platforms to find their tradeoff, which provide practical guidance for which
solutions to use under given performance goals.
VSketcher v1.0.8 Portable Crack CCS Concepts: • Networks → Network measurement; Network monitoring;
Additional Key Words and Phrases: Network Traffic Measurement;Big Network Data; Generalized
ACM Reference Format:
You Zhou, Youlin Zhang, Chaoyi Ma, Shigang Chen, and Olufemi O Odegbile. 2019. Generalized
Sketch Families for Network Traffic Measurement. Proc. ACM Meas. Anal. Comput. Syst. 3, 3,
Article 51 (December 2019), 34 pages. https://doi.org/10.1145/3366699
Traffic measurement is a critical function for network management, resource alignment,
traffic engineering, and anomaly detection. In particular, flow size and flow spread provide
fine-grained and general-purpose information from which a variety of traffic statistics can
∗You Zhou and Youlin Zhang are co-first authors
Authors’ addresses: You Zhou, [email protected], University of Florida, Gainsville, FL, USA; Youlin
Zhang, [email protected], University of Florida, Gainsville, FL, USA; Chaoyi Ma, [email protected], University
of Florida, Gainsville, FL, USA; Shigang Chen, [email protected], University of Florida, Gainsville, FL,
USA; Olufemi O Odegbile, [email protected], University of Florida, Gainsville, FL, USA.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted
without fee provided that copies are not made or distributed for profit or commercial advantage and that
copies bear this notice and the full citation on the first page. Copyrights for components of this work
owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise,
or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee.
VSketcher v1.0.8 Portable Cracked Generalized Sketch Families for Network Traffic Measurement 51:3
generality in performance tradeoff? In addition to space efficiency, we also want to keep
per-packet processing time to a minimum constant, such as a single memory update per
packet. Constant processing steps (time) for each packet helps avoid pipeline stalls and thus
Our goal is to meet all the above challenges by designing a family of per-flow measurement
solutions that share a common implementation structure, which can be augmented for
different tradeoff requirements, measuring both flow size and flow spread with constant
per-packet overhead (down to one memory update and a couple of hashes), and doing so in
tight memory (down to several bits per flow). In addition, we want to support network-wide
distributed measurement. No prior art has achieved such a goal. UnivMon  does not
perform per-flow measurement, nor does it consider flow spread. It incurs variable processing
time with numerous memory accesses per packet in the worst case. Elastic Sketch  does
not measure flow spread and incurs variable processing overhead. SketchLearner  needs
to update l + 1 sketches per packet, each requiring multiple memory accesses, with variable
per-packet time, and it does not consider spread, where l is the number of bits in flow
ID. The above work can estimate the sizes of flows, not the spreads of individual flows.
None of them provides a generalized structure for accuracy/throughput/space/processing
tradeoff. FlowRadar  provides a constant-time solution, but cannot fully address the
space challenge faced by its NetFlow-type design, which is less efficient than the spacesharing sketches that we adopt. RHHH  monitors hierarchical heavy hitters with constant
per-packet time (many memory accesses), but it does not consider flow spread or per-flow
measurement. Our work is complementary to OpenSketch  and SketchVisor , and
can be integrated into their architectures.
Numerous sketches have been proposed for traffic measurement [1, 5, 6, 9, 12–15, 19,
34, 39, 42, 62], each designed under specific performance requirements. This paper does
not intend to propose yet another sketch as numerous existing ones fail in fulfilling the
goal stated previously. Instead, we take a different path towards introducing meta-level
frameworks that incorporate the seemingly independent past (and future) sketches under a
common implementation structure, with the flexibility of plug-n-play and many options for
tradeoff. With this idea in mind, we propose several families of measurement solutions, using
the existing sketches as building components, and we compare them to find their respective
areas of excellence in a multi-dimensional evaluation space of measurement accuracy, memory
efficiency, processing overhead, online throughput and query performance. The contributions
are summarized below.
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First, we propose a family of sketches called bSketch based on the structure underlying the
counting Bloom filter, and another family called cSketch based on the structure of CountMin.
These sketches share the same implementation, with different plug-in data structures (e.g.,
counters, bitmaps , HLL  or a combination of them) for actual packet recording.
They support measurement of flow size/spread or multiple measurement tasks together.
Second, we introduce a family of virtual sketches called vSketch, also using a plug-in
design, which shares memory at a fine level, enabling per-flow measurement for both size
and spread with one memory update per packet in tight memory averaging several bits per
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Third, we discuss how to apply these sketch families in distributed measurement.
Fourth, we implement the proposed sketch families in both hardware and software. We
compare their performance using trace-based experiments and reveal the multi-dimensional
tradeoff that these sketches represent. Our experimental results shed light on how to choose
different sketches based on performance and application goals.
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