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Abstract:
Nearest neighbor load balancing algorithms, like diffusion, are
popular due to their simplicity, flexibility, and robustness. We show
that they are also asymptotically very efficient when a random rather
than a worst case initial load distribution is considered. We show
that diffusion needs $\Th{(\log n)^{2/d}}$ balancing time on a
$d$-dimensional mesh network with $n^d$ processors. Furthermore, some
but not all of the algorithms known to perform better than diffusion
in the worst case also perform better for random loads. We also
present new results on worst case performance regarding the maximum
load deviation.
