首页> 外文会议>International Workshop on Algorithms and Data Structures(WADS 2007); 20070815-17; Hailifax(CA) >The k-Resource Problem on Uniform and on Uniformly Decomposable Metric Spaces
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The k-Resource Problem on Uniform and on Uniformly Decomposable Metric Spaces

机译:一致和一致可分解度量空间上的k资源问题

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摘要

We define a natural generalization of the prominent fc-server problem, the fc-resource problem. It occurs in metric spaces with some demands and resources given at its points. The demands may vary with time, but the total demand may never exceed fc. The goal of an online algorithm is to satisfy demands by moving resources, while minimizing the cost for transporting resources.We give an asymptotically optimal O(log(min{n, fc}))-competitive randomized algorithm and an O(min{k, n})-competitivedeterministic one for the k-resource problem on uniform metric spaces consisting of n points. This extends known results for paging to the more general setting of k-resource. Basing on the results for uniform metric spaces, we develop a randomized algorithm solving the k-resource and the k-server problem on metric spaces which can be decomposed into components far away from each other. The algorithm achieves a competitive ratio of O(log(min{n, k})), provided that it has some extra resources more than the optimal algorithm.
机译:我们定义了突出的fc服务器问题,fc资源问题的自然概括。它发生在度量空间中,并在其点处给出了一些需求和资源。需求可能随时间变化,但总需求不得超过fc。在线算法的目标是通过移动资源来满足需求,同时最大程度地减少资源的运输成本。我们给出了一种渐近最优的竞争性O(log(min {n {n,fc}))-竞争性随机算法和一个O(min {k ,n})-关于由n个点组成的统一度量空间上k资源问题的竞争确定性。这将用于分页的已知结果扩展到k资源的更一般的设置。基于统一度量空间的结果,我们开发了一种随机算法,可以解决度量空间上的k资源和k服务器问题,这些问题可以分解为彼此远离的分量。只要它比最佳算法具有更多的额外资源,该算法就可以达到O(log(min {n,k}))的竞争比。

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