Distributed Computing and Networking: 14th International by Sanjit A. Seshia (auth.), Davide Frey, Michel Raynal,

By Sanjit A. Seshia (auth.), Davide Frey, Michel Raynal, Saswati Sarkar, Rudrapatna K. Shyamasundar, Prasun Sinha (eds.)

This publication constitutes the refereed court cases of the 14th foreign convention on disbursed Computing and Networking, ICDCN 2013, held in Mumbai, India, in the course of January 3-6, 2013. The 27 revised complete papers, five brief papers offered including 7 poster papers have been conscientiously reviewed and chosen from 149 submissions. The papers hide issues resembling dispensed algorithms and concurrent information buildings; integration of heterogeneous instant and stressed out networks; dispensed working platforms; internetworking protocols and net functions; disbursed database structures; cellular and pervasive computing, context-aware dispensed structures; embedded disbursed platforms; subsequent new release and converged community architectures; experiments and function overview of disbursed structures; overlay and peer-to-peer networks and companies; fault-tolerance, reliability, and availability; domestic networking and companies; multiprocessor and multi-core architectures and algorithms; source administration and caliber of provider; self-organization, self-stabilization, and autonomic computing; community safeguard and privateness; excessive functionality computing, grid computing, and cloud computing; energy-efficient networking and shrewdpermanent grids; safeguard, cryptography, and online game thought in allotted structures; sensor, PAN and ad-hoc networks; and site visitors engineering, pricing, community management.

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Proof. The algorithm for computing PageRank on directed graph also comprises of three phases. Combining the running time of these three phases from above we get the total time taken to finish the algorithm: O(λ + logλn + λ) rounds with high probability. Choosing λ = log n , gives the required bound as O( log n ). Fast Distributed PageRank Computation 6 25 Conclusion We presented fast distributed algorithms for computing PageRank, a measure of fundamental interest in networks. Our algorithms are Monte-Carlo and based on the idea of speeding up random walks in a distributed network.

Inside pagerank. ACM Trans. Internet Technol. 5(1), 92–128 (2005) 7. : The anatomy of a large-scale hypertextual web search engine. In: Seventh International World-Wide Web Conference (WWW 1998), pp. 107–117 (1998) 8. : Calculation of pagerank over a peer-to-peer network (2004) 9. : Efficient distributed random walks with applications. In: PODC, pp. 201–210 (2010) 10. : A note on the pagerank of undirected graphs. 1960 (2012) 11. : When the web meets the cell: using personalized pagerank for analyzing protein interaction networks.

2 Model of Computation and Definitions System Model. There are n processors, each with a unique identifier (id) from set P = [n]. We refer to the processor with id i as processor i. The system is synchronous and processors communicate by exchanging reliable messages. Computation is structured in terms of synchronous rounds, where in each round a processor can send and receive messages, and perform local polynomial computation, where the local computation time is assumed to be negligible compared to message latency.

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