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Type: Conference paper
Title: Unconstrained and constrained fault-tolerant resource allocation
Author: Liao, K.
Shen, H.
Citation: Computing and Combinatorics: 17th Annual International Conference, COCOON 2011: Dallas, TX, USA, August 14-16, 2011: Proceedings / Bin Fu, Ding-Zhu Du (eds.): pp.555-566
Publisher: Springer
Publisher Place: Germany
Issue Date: 2011
Series/Report no.: Lecture Notes in Computer Science ; 6842
ISBN: 9783642226847
ISSN: 0302-9743
Conference Name: International Computing & Combinatorics Conference (17th : 2011 : Dallas, Texas)
Statement of
Kewen Liao and Hong Shen
Abstract: First, we study the Unconstrained Fault-Tolerant Resource Allocation (UFTRA) problem (a.k.a. FTFA problem in [19]). In the problem, we are given a set of sites equipped with an unconstrained number of facilities as resources, and a set of clients with set R as corresponding connection requirements, where every facility belonging to the same site has an identical opening (operating) cost and every client-facility pair has a connection cost. The objective is to allocate facilities from sites to satisfy R at a minimum total cost. Next, we introduce the Constrained Fault-Tolerant Resource Allocation (CFTRA) problem. It differs from UFTRA in that the number of resources available at each site i is limited by Ri . Both problems are practical extensions of the classical Fault-Tolerant Facility Location (FTFL) problem [10]. For instance, their solutions provide optimal resource allocation (w.r.t. enterprises) and leasing (w.r.t. clients) strategies for the contemporary cloud platforms. In this paper, we consider the metric version of the problems. For UFTRA with uniform R, we present a star-greedy algorithm. The algorithm achieves the approximation ratio of 1.5186 after combining with the cost scaling and greedy augmentation techniques similar to [3,14], which significantly improves the result of [19] using a phase-greedy algorithm. We also study the capacitated extension of UFTRA and give a factor of 2.89. For CFTRA with uniform R, we slightly modify the algorithm to achieve 1.5186-approximation. For a more general version of CFTRA, we show that it is reducible to FTFL using linear programming. © 2011 Springer-Verlag.
Rights: © Springer-Verlag Berlin Heidelberg 2011
DOI: 10.1007/978-3-642-22685-4_48
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