By Mohsen Ghaffari, Fabian Kuhn (auth.), Yehuda Afek (eds.)
This publication constitutes the complaints of the twenty seventh foreign Symposium on dispensed Computing, DISC 2013, held in Jerusalem, Israel, in October 2013. The 27 complete papers offered during this quantity have been rigorously reviewed and chosen from 142 submissions; sixteen short bulletins also are incorporated. The papers are geared up in topical sections named: graph disbursed algorithms; topology, chief election, and spanning bushes; software program transactional reminiscence; shared reminiscence executions; shared reminiscence and garage; gossip and rumor; shared reminiscence initiatives and information buildings; routing; radio networks and the SINR version; crypto, belief, and impression; and networking.
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Additional resources for Distributed Computing: 27th International Symposium, DISC 2013, Jerusalem, Israel, October 14-18, 2013. Proceedings
In the streaming setting, we see a stream of elements coming one at a time and the goal is to compute the number of distinct elements in the stream using as little memory as possible. In , Flajolet et al. 5KB, and achieve a relative accuracy of 2%, compared with the 109 bytes of memory required if we want to compute F0 exactly. Similar situations happen in the distributed communication setting, where we have k sites, each holding a set of elements from the universe [n], and the sites want to compute the number of distinct elements of the union of their k data sets.
5 This can be done for example by picking the cut which passed the test and for which the related component has the smallest id among all the cuts that passed the test. 10 M. Ghaffari and F. Kuhn Moreover, this knowledge is consistent between all the nodes of component C. After Θ(log n/δ 2 ) experiments, each node v of component C considers the test passed iff v noticed (C, V \C)∩Ej = ∅ in at most half of the experiments. We defer the calculations of the proof of Lemma 1 to of the full version .
Distributed verification and hardness of distributed approximation. SIAM J. on Comp. 41(5), 1235–1265 (2012) 5. : Note on maximum flow through a network. IRE Transactions on Information Theory IT-2, 117–199 (1956) 6. : Distributed approximation: a survey. SIGACT News 35(4), 40–57 (2004) 7. : Flows in Networks. Princeton Univ. Press (2010) 8. : Maximal flow through a network. Canad. J. Math. 8, 399–404 (1956) 9. : A matroid approach to finding edge connectivity and packing arborescences. In: Proc.