Improved approximation algorithms for constrained fault-tolerant resource allocation

Journal article

Liao, Kewen, Shen, Hong and Guo, Longkun. (2015). Improved approximation algorithms for constrained fault-tolerant resource allocation. Theoretical Computer Science. 590, pp. 118-128. https://doi.org/10.1016/j.tcs.2015.02.029
AuthorsLiao, Kewen, Shen, Hong and Guo, Longkun
Abstract

In Constrained Fault-Tolerant Resource Allocation (FTRA) problem, we are given a set of sites containing facilities as resources and a set of clients accessing these resources. Each site i can open at most facilities with opening cost . Each client j requires an allocation of open facilities and connecting j to any facility at site i incurs a connection cost . The goal is to minimize the total cost of this resource allocation scenario. FTRA generalizes the Unconstrained Fault-Tolerant Resource Allocation () [1] and the classical Fault-Tolerant Facility Location (FTFL) [2] problems: for every site i, does not have the constraint , whereas FTFL sets . These problems are said to be uniform if all 's are the same, and general otherwise. For the general metric FTRA, we first give an LP-rounding algorithm achieving an approximation ratio of 4. Then we show the problem reduces to FTFL, implying the ratio of 1.7245 from [3]. For the uniform FTRA, we provide a 1.52-approximation primal–dual algorithm in time, where n is the total number of sites and clients.

Keywordsresource allocation; approximation algorithms; LP-rounding; reduction; primal–dual; time complexity
Year2015
JournalTheoretical Computer Science
Journal citation590, pp. 118-128
PublisherElsevier
ISSN0304-3975
Digital Object Identifier (DOI)https://doi.org/10.1016/j.tcs.2015.02.029
Scopus EID2-s2.0-84944732001
Research or scholarlyResearch
Page range118-128
FunderAustralian Research Council (ARC)
Publisher's version
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All rights reserved
File Access Level
Controlled
Output statusPublished
Publication dates
Online23 Feb 2015
Publication process dates
Accepted18 Feb 2015
Deposited09 Aug 2021
ARC Funded ResearchThis output has been funded, wholly or partially, under the Australian Research Council Act 2001
Grant IDARC/DP0985063
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