6,267 research outputs found
Dependability in Aggregation by Averaging
Aggregation is an important building block of modern distributed
applications, allowing the determination of meaningful properties (e.g. network
size, total storage capacity, average load, majorities, etc.) that are used to
direct the execution of the system. However, the majority of the existing
aggregation algorithms exhibit relevant dependability issues, when prospecting
their use in real application environments. In this paper, we reveal some
dependability issues of aggregation algorithms based on iterative averaging
techniques, giving some directions to solve them. This class of algorithms is
considered robust (when compared to common tree-based approaches), being
independent from the used routing topology and providing an aggregation result
at all nodes. However, their robustness is strongly challenged and their
correctness often compromised, when changing the assumptions of their working
environment to more realistic ones. The correctness of this class of algorithms
relies on the maintenance of a fundamental invariant, commonly designated as
"mass conservation". We will argue that this main invariant is often broken in
practical settings, and that additional mechanisms and modifications are
required to maintain it, incurring in some degradation of the algorithms
performance. In particular, we discuss the behavior of three representative
algorithms Push-Sum Protocol, Push-Pull Gossip protocol and Distributed Random
Grouping under asynchronous and faulty (with message loss and node crashes)
environments. More specifically, we propose and evaluate two new versions of
the Push-Pull Gossip protocol, which solve its message interleaving problem
(evidenced even in a synchronous operation mode).Comment: 14 pages. Presented in Inforum 200
Spectra: Robust Estimation of Distribution Functions in Networks
Distributed aggregation allows the derivation of a given global aggregate
property from many individual local values in nodes of an interconnected
network system. Simple aggregates such as minima/maxima, counts, sums and
averages have been thoroughly studied in the past and are important tools for
distributed algorithms and network coordination. Nonetheless, this kind of
aggregates may not be comprehensive enough to characterize biased data
distributions or when in presence of outliers, making the case for richer
estimates of the values on the network. This work presents Spectra, a
distributed algorithm for the estimation of distribution functions over large
scale networks. The estimate is available at all nodes and the technique
depicts important properties, namely: robust when exposed to high levels of
message loss, fast convergence speed and fine precision in the estimate. It can
also dynamically cope with changes of the sampled local property, not requiring
algorithm restarts, and is highly resilient to node churn. The proposed
approach is experimentally evaluated and contrasted to a competing state of the
art distribution aggregation technique.Comment: Full version of the paper published at 12th IFIP International
Conference on Distributed Applications and Interoperable Systems (DAIS),
Stockholm (Sweden), June 201
Fast Distributed Computation of Distances in Networks
This paper presents a distributed algorithm to simultaneously compute the
diameter, radius and node eccentricity in all nodes of a synchronous network.
Such topological information may be useful as input to configure other
algorithms. Previous approaches have been modular, progressing in sequential
phases using building blocks such as BFS tree construction, thus incurring
longer executions than strictly required. We present an algorithm that, by
timely propagation of available estimations, achieves a faster convergence to
the correct values. We show local criteria for detecting convergence in each
node. The algorithm avoids the creation of BFS trees and simply manipulates
sets of node ids and hop counts. For the worst scenario of variable start
times, each node i with eccentricity ecc(i) can compute: the node eccentricity
in diam(G)+ecc(i)+2 rounds; the diameter in 2*diam(G)+ecc(i)+2 rounds; and the
radius in diam(G)+ecc(i)+2*radius(G) rounds.Comment: 12 page
Estimating value at risk and optimal hedge ratio in Latin markets: a copula-based GARCH approach
In this paper we use a copula-based GARCH model to estimate conditional variances and covariances of the bivariate relationships between U.S. market with Brazilian, Argentinean and Mexican markets. To that we used daily prices of S&P500, Ibovespa, Merval and IPC from January 2009 to December 2010, totaling 483 observations. The results allows to conclude that both the volatility of Latin markets, such as its dependence with the U.S. decreased in the period, resulting in lower estimates for the VaR and Hedge, compared with those based on the unconditional variance and covariance, emphasizing that after theeffects of the 2007/2008 U.S. crisis, these Latin markets can again be considered as options for international diversification for investors with assets of the U.S. market in their portfolio.Value at risk, Hedge ratio, Copula, Latin markets
Tratamento da gestação ectópica com metotrexate
Trabalho de ConclusĂŁo de Curso - Universidade Federal de Santa Catarina. Curso de Medicina. Departamento de Tocoginecologia
Approaches to Conflict-free Replicated Data Types
Conflict-free Replicated Data Types (CRDTs) allow optimistic replication in a
principled way. Different replicas can proceed independently, being available
even under network partitions, and always converging deterministically:
replicas that have received the same updates will have equivalent state, even
if received in different orders. After a historical tour of the evolution from
sequential data types to CRDTs, we present in detail the two main approaches to
CRDTs, operation-based and state-based, including two important variations, the
pure operation-based and the delta-state based. Intended as a tutorial for
prospective CRDT researchers and designers, it provides solid coverage of the
essential concepts, clarifying some misconceptions which frequently occur, but
also presents some novel insights gained from considerable experience in
designing both specific CRDTs and approaches to CRDTs.Comment: 36 page
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