Solving atomic multicast when groups crash

In this paper, we study the atomic multicast problem, a fundamental abstraction for building faulttolerant systems. In the atomic multicast problem, the system is divided into non-empty and disjoint groups of processes. Multicast messages may be addressed to any subset of groups, each message possibly being multicast to a different subset. Several papers previously studied this problem either in local area networks [3, 9, 20] or wide area networks [13, 21]. However, none of them considered atomic multicast when groups may crash. We present two atomic multicast algorithms that tolerate the crash of groups. The first algorithm tolerates an arbitrary number of failures, is genuine (i.e., to deliver a message m, only addressees of m are involved in the protocol), and uses the perfect failures detector P. We show that among realistic failure detectors, i.e., those that do not predict the future, P is necessary to solve genuine atomic multicast if we do not bound the number of processes that may fail. Thus, P is the weakest realistic failure detector for solving genuine atomic multicast when an arbitrary number of processes may crash. Our second algorithm is non-genuine and less resilient to process failures than the first algorithm but has several advantages: (i) it requires perfect failure detection within groups only, and not across the system, (ii) as we show in the paper it can be modified to rely on unreliable failure detection at the cost of a weaker liveness guarantee, and (iii) it is fast, messages addressed to multiple groups may be delivered within two inter-group message delays only

Multi-Dimensional Self-Organizing Maps on Massively Parallel Hardware

Although available (sequential) computer hardware is very powerful nowadays, the implementation of artificial neural networks on massively parallel hardware is still undoubtedly of high interest, not only under an academic point of view. This paper presents an implementation of multi-dimensional Self-Organizing Maps on a scalable SIMD structure of a CNAPS computer with up to 512 parallel processors.

Ordering of the RGB space with a growing self-organizing network. Application to color mathematical morphology

Mathematical morphology is broadly used in image processing, but it is mainly restricted to binary or greyscale images. Extension to color images is not straightforward due to the need of application to an ordered space with an infimum and a supremum. In this paper a new approach for the ordering of the RGB space is presented. The adaptation of a linear growing self-organizing network to the three-dimensional color space allows the definition of an order relationship among colors. This adaptation is measured with the topographic product to guarantee a good topology-preservation of the RGB space. Once an order has been established, several examples of application of mathematical morphology operations to color images are presented.This work has been partially funded by the Spanish Ministry of Science and Technology under project DPI2002-04434-C04-01

Estimating Relevant Input Dimensions for Self-organizing Algorithms

this paper. Approaches like [11] clearly indicate that often a considerable reduction of the data dimension is possible without loss of informatio

Orion: A Generic Model and Tool for Data Mining

International audienc