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Server Placement in the Presence of Competition

机译:竞争中的服务器放置

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摘要

This paper addresses the optimization problems of placing servers in the presence of competition. We place a set of extra servers on a graph to compete with the set of original servers. Our objective is to find the placement that maximizes the benefit, which is defined as the profits from the requests made to the extra servers despite the competition, minus the cost of constructing those extra servers.We propose an O(| V|~з κ) time dynamic programming algorithm to find the optimal placement of κ extra servers that maximizes the benefit in a tree with V nodes. We also propose an O(|V|~з) time dynamic programming algorithm for finding the optimal placement of extra servers that maximizes the benefit, without any constraint on the number of extra servers. For general connected graphs, we prove that the optimization problems are NP-complete. As a result, we present a greedy heuristic for the problems. Experiment results indicate that the greedy heuristic achieves good results, even when compared with the upper bounds found by a linear programming algorithm. The greedy heuristic yields performances within 15% of the upper bound in the worst case, and within 2% of the same theoretical upper bound on average.
机译:本文解决了在竞争中放置服务器的优化问题。我们在图表上放置了一组额外的服务器,以与原始服务器竞争。我们的目标是找到一个最大化收益的布局,该收益被定义为尽管存在竞争,但从对额外服务器的请求中获得的利润减去建造这些额外服务器的成本。我们提出O(| V |〜зκ时间动态规划算法,以找到κ个额外服务器的最佳位置,从而最大化具有V个节点的树的收益。我们还提出了一种O(| V |〜з)时间动态规划算法,用于寻找额外服务器的最佳位置,从而最大化收益,而对额外服务器的数量没有任何限制。对于一般的连通图,我们证明了优化问题是NP完全的。结果,我们提出了针对这些问题的贪婪启发式方法。实验结果表明,即使与线性规划算法发现的上限相比,贪婪的启发式方法也能取得良好的效果。在最坏的情况下,贪婪的启发式方法会使性能下降到上限的15%以内,而平均而言,在相同理论上限的2%以内。

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