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Distributed approximation of k-service assignment

Distributed approximation of k-service assignment We consider the k-Service Assignment problem ( $$k$$ k -SA). The input consists of a network that contains servers and clients. Associated with each client is a demand and a profit. In addition, each client c has a service requirement[InlineEquation not available: see fulltext.], where $$\kappa (c)$$ κ ( c ) is a positive integer. A client c is satisfied only if its demand is handled by exactly $$\kappa (c)$$ κ ( c ) neighboring servers. The objective is to maximize the total profit of satisfied clients, while obeying the given capacity limits of the servers. We focus here on the more challenging case of hard constraints, where no profit is granted for partially satisfied clients. This models, e.g., when a client wants, for reasons of fault tolerance, a file to be stored at $$\kappa (c)$$ κ ( c ) or more nearby servers. Other motivations from the literature include resource allocation in 4G cellular networks and machine scheduling on related machines with assignment restrictions. In the r-restricted version of $$k$$ k -SA, no client requires more than an r-fraction of the capacity of any adjacent server. We present a (centralized) polynomial-time [InlineEquation not available: see fulltext.]-approximation algorithm for r-restricted $$k$$ k -SA. A variant of this algorithm achieves an approximation ratio of [InlineEquation not available: see fulltext.] when given a resource augmentation factor of $$1+r$$ 1 + r . We use the latter result to present a [InlineEquation not available: see fulltext.]-approximation algorithm for $$k$$ k -SA. In the distributed setting, we present: (i) a [InlineEquation not available: see fulltext.]-approximation algorithm for r-restricted $$k$$ k -SA, (ii) a [InlineEquation not available: see fulltext.]-approximation algorithm that uses a resource augmentation factor of $$1+r$$ 1 + r for r-restricted $$k$$ k -SA, both for any constant $$\varepsilon >0$$ ε > 0 , and (iii) an [InlineEquation not available: see fulltext.]-approximation algorithm for $$k$$ k -SA (in expectation). The three distributed algorithms run in $$O(k^2 \varepsilon ^{-2} \log ^3 n)$$ O ( k 2 ε - 2 log 3 n ) synchronous rounds (with high probability). In particular, this yields the first distributed [InlineEquation not available: see fulltext.]-approximation of $$1$$ 1 -SA. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Distributed Computing Springer Journals

Distributed approximation of k-service assignment

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References (23)

Publisher
Springer Journals
Copyright
Copyright © 2017 by Springer-Verlag GmbH Germany, part of Springer Nature
Subject
Computer Science; Computer Communication Networks; Computer Hardware; Computer Systems Organization and Communication Networks; Software Engineering/Programming and Operating Systems; Theory of Computation
ISSN
0178-2770
eISSN
1432-0452
DOI
10.1007/s00446-017-0321-3
Publisher site
See Article on Publisher Site

Abstract

We consider the k-Service Assignment problem ( $$k$$ k -SA). The input consists of a network that contains servers and clients. Associated with each client is a demand and a profit. In addition, each client c has a service requirement[InlineEquation not available: see fulltext.], where $$\kappa (c)$$ κ ( c ) is a positive integer. A client c is satisfied only if its demand is handled by exactly $$\kappa (c)$$ κ ( c ) neighboring servers. The objective is to maximize the total profit of satisfied clients, while obeying the given capacity limits of the servers. We focus here on the more challenging case of hard constraints, where no profit is granted for partially satisfied clients. This models, e.g., when a client wants, for reasons of fault tolerance, a file to be stored at $$\kappa (c)$$ κ ( c ) or more nearby servers. Other motivations from the literature include resource allocation in 4G cellular networks and machine scheduling on related machines with assignment restrictions. In the r-restricted version of $$k$$ k -SA, no client requires more than an r-fraction of the capacity of any adjacent server. We present a (centralized) polynomial-time [InlineEquation not available: see fulltext.]-approximation algorithm for r-restricted $$k$$ k -SA. A variant of this algorithm achieves an approximation ratio of [InlineEquation not available: see fulltext.] when given a resource augmentation factor of $$1+r$$ 1 + r . We use the latter result to present a [InlineEquation not available: see fulltext.]-approximation algorithm for $$k$$ k -SA. In the distributed setting, we present: (i) a [InlineEquation not available: see fulltext.]-approximation algorithm for r-restricted $$k$$ k -SA, (ii) a [InlineEquation not available: see fulltext.]-approximation algorithm that uses a resource augmentation factor of $$1+r$$ 1 + r for r-restricted $$k$$ k -SA, both for any constant $$\varepsilon >0$$ ε > 0 , and (iii) an [InlineEquation not available: see fulltext.]-approximation algorithm for $$k$$ k -SA (in expectation). The three distributed algorithms run in $$O(k^2 \varepsilon ^{-2} \log ^3 n)$$ O ( k 2 ε - 2 log 3 n ) synchronous rounds (with high probability). In particular, this yields the first distributed [InlineEquation not available: see fulltext.]-approximation of $$1$$ 1 -SA.

Journal

Distributed ComputingSpringer Journals

Published: Dec 30, 2017

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