Combinatorial algorithms for data migration to minimize average completion time

TL;DR: A simple algorithm is presented that achieves an approximation ratio of ${sqrt{2}}$ ≈ 1.414, thus improving the 1.796-approximation given by Gandhi et al. (ACM Transaction on Algorithms, 2(1):116-129, 2006).

Abstract: The data migration problem is to compute an efficient plan for moving data stored on devices in a network from one configuration to another. It is modeled by a transfer graph, where vertices represent the storage devices, and the edges represent the data transfers required between pairs of devices. Each vertex has a non-negative weight, and each edge has unit processing time. A vertex completes when all the edges incident on it complete; the constraint is that two edges incident on the same vertex cannot be processed simultaneously. The objective is to minimize the sum of weighted completion times of all vertices. Kim (Journal of Algorithms, 55:42-57, 2005) gave an LP-rounding 3-approximation algorithm. We give a more efficient primal-dual algorithm that achieves the same approximation guarantee, which can be extended to yield a 5.83-approximation for arbitrary processing times. We also study a variant of the open shop scheduling problem. This is a special case of the data migration problem in which the transfer graph is bipartite and the objective is to minimize the completion times of edges. We present a simple algorithm that achieves an approximation ratio of ${\sqrt{2}}$ ≈ 1.414, thus improving the 1.796-approximation given by Gandhi et al. (ACM Transaction on Algorithms, 2(1):116-129, 2006). We show that the analysis of our algorithm is almost tight.