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M.S. Thesis Defense: Alireza Sheikhattar
Tuesday, July 15, 2014
3:00 p.m.
AVW 2328
For More Information:
Maria Hoo
301 405 3681
mch@umd.edu

ANNOUNCEMENT: M.S. Thesis Defense

Name: Alireza Sheikhattar

Committee:
Professor Mehdi Kalantari Khandani
Professor Mark Shayman
Professor Richard La

Date/Time: Tuesday, July 15, 2014 at 3:00 p.m.

Location: AVW 2168
 
Title: Distributed Load Balancing Algorithm in Wireless Networks

Abstract:
As communication networks scale up in size, complexity and demand, effective distribution of the traffic load throughout the network is a matter of great importance. Load balancing will enhance the network throughput and enables us to utilize both communication and energy resources more evenly through an efficient redistribution of traffic load across the network. This thesis provides an algorithm for balancing the traffic load in a general network setting. Unlike most of state-of-the-art algorithms in load balancing context, the proposed method is fully distributed, eliminating the need to collect information at a central node. The effective distribution of load is realized through solving a convex optimization problem where the p-norm of network load is minimized subject to network physical constraints. Our optimization solution relies on the Alternating Direction Method of Multipliers (ADMM), which is a powerful tool for solving distributed convex optimization problems. The distributed implementation of our proposed algorithm is further elaborated by introducing a projection step and an initialization setup. The projection step involves an inner-loop iterative scheme to solve linear subproblems. A fast convergent iterative scheme is presented which shows superior convergence performance compared to conventional methods. Inspired by fast propagation of waves in physical media, this iterative scheme is derived from partial differential equations for propagation of electrical voltages and currents in a transmission line. Finally, the initialization setup stage is developed in order to overcome the last obstacle toward achieving a fully distributed algorithm.

This Event is For: Graduate • Faculty

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