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          <dc:title>Distributed Approximation of k-Service Assignment</dc:title>
          <dc:creator>Halldórsson, Magnús M.</dc:creator>
          <dc:creator>Köhler, Sven</dc:creator>
          <dc:creator>Rawitz, Dror</dc:creator>
          <dc:subject>approximation algorithms</dc:subject>
          <dc:subject>distributed algorithms</dc:subject>
          <dc:subject>related machines</dc:subject>
          <dc:description>We consider the k-Service Assignment problem (k-SA), defined as follows. The input consists of a network that contains servers and clients, and an integer k. Each server has a finite capacity, and each client is associated with a demand and a profit. A feasible solution is an assignment of clients to neighboring servers such that (i) the total demand assigned to a server is at most its capacity, and (ii) a client is assigned either to k servers or to none. The profit of an assignment is the total profit of clients that are assigned to k servers, and the goal is to find a maximum profit assignment. In the r-restricted version of k-SA, no client requires more than an r-fraction of the capacity of any adjacent server. The k-SA problem is motivated by backup placement in networks and by resource allocation in 4G cellular networks. It can also be viewed as machine scheduling on related machines with assignment restrictions. &#13;
&#13;
We present a centralized polynomial time greedy (k+1-r)/(1-r)-approximation algorithm for r-restricted k-SA. We then show that a variant of this algorithm achieves an approximation ratio of k+1 using a resource augmentation factor of 1+r. We use the latter to present a (k+1)^2-approximation algorithm for k-SA. In the distributed setting, we present: (i) a (1+epsilon)*(k +1-r)/(1-r)-approximation algorithm for r-restricted k-SA, (ii) a (1+epsilon)(k+1)-approximation algorithm that uses a resource augmentation factor of 1+r for r-restricted k-SA, both for any constant epsilon&gt;0, and (iii) an O{k^2}-approximation algorithm for k-SA (in expectation). The three distributed algorithms compute a solution with high probability and terminate in O(k^2 *log^3(n)) rounds.</dc:description>
          <dc:publisher>Schloss Dagstuhl – Leibniz-Zentrum für Informatik</dc:publisher>
          <dc:contributor>Magnús M. Halldórsson and Sven Köhler and Dror Rawitz</dc:contributor>
          <dc:date>2016</dc:date>
          <dc:relation>Is Part Of LIPIcs, Volume 46, 19th International Conference on Principles of Distributed Systems (OPODIS 2015)</dc:relation>
          <dc:type>InProceedings</dc:type>
          <dc:type>Text</dc:type>
          <dc:type>doc-type:ResearchArticle</dc:type>
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          <dc:identifier>doi:10.4230/LIPIcs.OPODIS.2015.11</dc:identifier>
          <dc:identifier>urn:nbn:de:0030-drops-66024</dc:identifier>
          <dc:identifier>https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.OPODIS.2015.11</dc:identifier>
          <dc:language>eng</dc:language>
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