An Anytime Distributed Load-Balancing Algorithm Satisfying Capacity and Quantization Constraints

Current research in the field of distributed consensus algorithms fails to adequately address physical limitations of real systems. This paper proposes a new algorithm for quantized distributed load balancing over a network of agents subject to upper-limit constraints. More precisely, loads are integer values, and nodes are constrained to remain under maximum load capacities at all times. Convergence to a set of balanced states is proven for all connected graphs with any feasible initial load distribution, given some conditions on the placement of nodes with finite capacity. We present simulations that verify our results and discuss possible extensions of the algorithm.

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