Distributed Computing and Networking: 10th International by Krithi Ramamritham (auth.), Vijay Garg, Roger Wattenhofer,

By Krithi Ramamritham (auth.), Vijay Garg, Roger Wattenhofer, Kishore Kothapalli (eds.)

This booklet constitutes the refereed court cases of the tenth overseas convention on allotted Computing and Networking, ICDCN 2009, held in Hyderabad, India, in the course of January 3-6, 2009.

The 20 papers and 32 brief displays offered including three keynote talks and a memorial lecture on A.K. Choudhury have been conscientiously reviewed and chosen from 179 submissions. the subjects addressed are sensor networks, multi-core and shared reminiscence, peer-to-peer-computing, reliability and protection, dispensed computing, community algorithms, fault tolerance and types, fault tolerance and replication, instant networks, and grid and cluster computing.

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Additional resources for Distributed Computing and Networking: 10th International Conference, ICDCN 2009, Hyderabad, India, January 3-6, 2009. Proceedings

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Contribution. In summary, the contributions of the paper are the following ones. – A simple and fully decentralized, VIrtual Networked COordinate System (VINCOS) that achieves a good coordinate assignment. – A Networked Geometric Structuring approach (NetGeoS) for autonomous systems. Here, we show how NetGeoS builds upon VINCOS. Large-Scale Networked Systems 27 Outline. After introducing our system model in Section 2, we present the design rationale of our approach and give an overview of related works in Section 3.

Let ci and pi be the coordinates and the partition number of node i, respectively. t. f : K → {0, . . , p}, where f (ci ) → pi . Let us observe that such a definition allows any node to compute the partition number of any other node whose coordinates are known: each node has a global foresight of the system layout. Let (x1 , x2 ) be the VINCOS coordinates of node i. 2, the following line partitioning function was used to produce the introductory example, where nodes were structured into North, South, and Equator groups (f : N ∗ N → {1, 2, 3}): f (x1 , x2 ) → 1 when x1 > x2 2 when x1 = x2 3 when x1 < x2 , where node i belongs to the North group if x1 < x2 , to the South group if x1 > x2 and to the Equator group if x1 = x2 (see Fig.

Let OP T denote the cost of an optimal solution to a given instance of UDG-FacLoc. Then cost(S ∗ ) ≤ (6 + B + ε) · OP T . 1 Analysis To analyze our algorithm we need some details of the Jain-Vazirani primal-dual algorithm used in Step 1. For a more complete description see [14]. The starting point of this algorithm is the following Integer Program (IP) representation of facility location. Here yi indicates whether facility i is open and xij indicates if city j is connected to facility i. The first set of constraints ensure that every city is connected to a facility and the second set of constraints guarantee that each city is connected to an open facility.

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