Applying The Universal Scalability Law to Distributed Systems
Neil J. Gunther
Performance Dynamics, Castro Valley, California, USA
When I originally developed the
Universal Scalability Law (USL),
it was in the context of tightly-coupled Unix multiprocessors, which
led to an inherent dependency between the serial contention term and the data consistency term in the
USL, i.e., no contention, no coherency penalty [1,2].
A decade later, I realized that the USL would have broader applicability to distributed clusters
if this dependency was removed .
In this talk I will show examples of how the
most recent version of the
USL (with three parameters α, β, γ) can be applied as a statistical regression
model to a variety of large-scale distributed systems, such as
Hadoop , Zookeeper, Sirius, AWS cloud, and Avalanche DLT,
in order to quantify their scalability in terms of numerical concurrency, contention,
and coherency values.
N. J. Gunther, "A Simple Capacity Model of Massively Parallel Transaction Systems,"
CMG Conference, San Diego (1993)
N. J. Gunther, The Practical Performance Analyst, McGraw-Hill (1998)
N. J. Gunther, Guerrilla Capacity Planning, Springer (2007)
N. J. Gunther, P. Puglia and K. Tomasette,
"Hadoop Superlinear Scalability: The perpetual motion of parallel performance,"
Communications of the ACM,
Vol. 58 No. 4, Pages 46-55 (2015)
Distributed Systems Conference
11:00 am IST, Saturday February 16, 2019
09:30 pm PST, Friday February 15, 2019
05:30 am UTC, Saturday February 16, 2019
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On 31 Dec 2018, 11:51.