Title: Hydrodynamic limits for randomized load balancing networks

Abstract:

Randomized load balancing algorithms allow for the efficient use of resources and are of particular importance in large-scale networks. Since these networks are too complex to be amenable to an exact analysis, an established framework is instead to obtain tractable approximations that provide qualitative insight into the dynamics, and whose accuracy can be rigorously justified via limit theorems in a suitable asymptotic regime. However, load balancing networks with jobs having general service distributions fall outside the purview of existing methods. We introduce a novel interacting particle representation for these networks, describe their hydrodynamic scaling limits, and show how they can be used to provide insight into both transient and equilibrium performance measures of the network. This talk is based on various joint works with Reza Aghajani and Pooja Agarwal.

Bio:

Kavita Ramanan is a professor of Applied Mathematics at Brown University.

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