The Mean Value Analysis algorithm for Queueing Network Theory, “Linearizer: A Heuristic Algorithm for Queueing Network Models of Computing Systems” by Chandy and Neuse is one of the most popular and extensively used algorithm if not the most popular. In Section 4 of the paper, the condition that is the primary reason for calling the algorithm “Linearizer” is stated. We will see how that condition implies the justification of the name.
The question may arise, why do we care to know this? The title having the word “Linearizer” as the first word implies that there must be a very important role played by the linearization proposed in the algorithm. In fact, my personal understanding is that linearization of a specific term in the algorithm is used as a heuristic to quickly estimate the number of jobs waiting for service. If the reader already understands the interaction between various other terms and the linearized term in the algorithm and how the linearization of one term is helping a faster estimate then he/she may ignore the rest.
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Dr. Jayanta Choudhury (LinkedIn), is a technology researcher at TeamQuest Corporation, focusing on capacity planning and performance modeling for IT resource optimization. He finished graduate school with a PhD degree from the University of Louisiana at Lafayette, in 2008. He has been working in the area of capacity planning and performance modeling of computing systems since 2007.
Dr. Jayanta Choudhury’s research interests include performance modeling, capacity planning, operations research, high performance computing, algorithm development, data analysis, numerical analysis, and numerical solution of PDEs, ODEs and their applications. He focuses on the fundamental principles behind the algorithms and methodologies used in the area of capacity planning and performance modeling.