As computer systems have become more complex, numerous competing approaches have been proposed for these systems to self-configure, self-manage, self-repair, etc. such that human intervention in their operation can be minimized. In ubiquitous systems this has always been a central issue as well. In this paper we overview techniques to implement self- ∗ properties in large-scale, decentralized networks through bio-inspired techniques in general, and gossip-based algorithms in particular. We believe that gossip-based algorithms could be an important inspiration for solving problems in ubiquitous computing as well. As an example, we outline a novel approach to arrange large numbers of mobile agents (e.g., vehicles, rescue teams carrying mobile devices) into different formations in a totally decentralized manner. The approach is inspired by the biological mechanism of cell sorting via differential adhesion, as well as by our earlier work in self-organizing peer-to-peer overlay networks.

In recent years, the peer-to-peer paradigm has gained momentum in several application areas: file-sharing and VoIP applications have been able to attract millions of end users, while large-scale distributed computing frameworks, including the Grid, have proven their ability of attacking large scientific problems. We believe, however, that the potential of the P2P approach has not been completely exploited yet. The goal of this position paper is to propose another scientific area where the P2P cooperation paradigm could be profitably adopted: network analysis, i.e. the mathematical characterization of the main graph-theoretic properties of a large-scale network. We discuss the potential issues that must be confronted with when a decentralized approach to network analysis is taken, and we propose a preliminary research plan. 1

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ter verkrijging van de graad van doctor aan de Technische Universiteit Delft, op gezag van de Rector Magnificus prof.ir. K.C.A.M. Luyben, voorzitter van het College voor Promoties, in het openbaar te verdedigen op dinsdag 1 maart 2011 om 12:30 uur door Michel MEULPOLDER ingenieur in de technische informatica geboren te Arnemuiden, NederlandDit proefschrift is goedgekeurd door de promotor:

Gossip plays a very significant role in human society. Information spreads throughout the human grapevine at an amazing speed, often reaching almost everyone in a community, without any central coordinator. Moreover, rumor tends to be extremely stubborn: once spread, it is nearly impossible to erase it. In many distributed computer systems—most notably in cloud computing and peer-to-peer computing—this speed and robustness, combined with algorithmic simplicity and the lack of central management, are very attractive features. Accordingly, over the past few decades several gossip-based algorithms have been developed to solve various problems. In this chapter, we focus on two main manifestations of gossip: information spreading (also known as multicast) where a piece of news is being spread, and information aggregation (or distributed data mining), where distributed information is being summarized. For both topics we discuss theoretical issues, mostly relying on results from epidemiology, and we also consider design issues and optimizations in distributed applications. Objectives • Explain the basic properties of gossip-based information dissemination • Show how the gossip approach can be used for another domain: information aggregation • Discuss example systems which are based on gossip or which apply components based on gossip

Abstract. We focus on the problem of data mining over large-scale fully distributed databases, where each node stores only one data record. We assume that a data record is never allowed to leave the node it is stored at. Possible motivations for this assumption include privacy or a lack of a centralized infrastructure. To tackle this problem, earlier we proposed the generic gossip learning framework (GoLF), but so far we have studied only basic linear algorithms. In this paper we implement the well-known boosting technique in GoLF. Boosting techniques have attracted growing attention in machine learning due to their outstanding performance in many practical applications. Here, we present an implementation of a boosting algorithm that is based on FilterBoost. Our main algorithmic contribution is a derivation of a pure online multi-class version of FilterBoost, so that it can be employed in GoLF. We also propose improvements to GoLF, with the aim of maximizing the diversity of the evolving models gossiped in the network, a feature that we show to be important. We evaluate the robustness and the convergence speed of the algorithm empirically over three benchmark databases. We compare the algorithm with the sequential AdaBoost algorithm and we test its performance in a failure scenario involving message drop and delay, and node churn.