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 [3]. 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 [4], 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)

DSConf 2019
Distributed Systems Conference
Pune, India
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.