Most algorithms for reconfigurable meshes (R-meshes) are based on the divide-and-conquer
(DAC) strategy. Although the strategy per se does not require the subproblems
to be equal in size, existing DAC algorithms for R-meshes do divide the problem
approximately evenly. This paper demonstrates that dividing a problem evenly is not
necessarily a good way to decompose a problem. There are occasions on which an
irregular decomposition scheme may be preferable. We take this approach and obtain a
new sorting algorithm. Our sorting algorithm has several strengths: it is simple, scalable,
and as broadcast-efficient as the best known result.
Rapid increases in the demand for broadband data are increasingly causing a growth in costs for communication service providers (CSPs). Yet under the current pricing plans, CSPs' revenue has not kept pace with these costs. Thus, many CSPs are considering Smart Data Pricing (SDP) as a way to reduce cost or increase revenue. Before offering such novel data plans, however, CSPs must conduct trials of the specific data plans proposed. Due to the complexity of necessary changes in network equipment and a need to carefully design the trial in order to understand customer behavior, planning such trials is not only a critical precursor to SDP deployment, but also a nontrivial undertaking in itself. This paper discusses general principles of trial design and proposes two methods for estimating their effectiveness. We first give an introduction to the goals of SDP research and review three possible SDP approaches. We then discuss the importance of pre-trial participant surveys and some technical considerations of implementing the trial infrastructure for a particular SDP algorithm. Finally, we show how the CSP may extrapolate from the trial results to estimate the SDP trial's benefits, in terms of changes in traffic patterns and a reduction in spectrum requirements. We conclude with some remarks about future work.
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