@mastersthesis {phdThesis,
	title = {Epidemic Algorithms for Large Scale Data Dissemination},
	year = {2013},
	month = {October},
	school = {University of Minho},
	type = {PhD Thesis},
	address = {Braga, Portugal},
	abstract = {<p>Distributed systems lie at the core of modern IT infrastructures and services, such as the Internet, e-commerce, the stock exchange, Cloud Computing and the SmartGrid. These systems, built and developed throughout the last decades, have relied, due to their importance, on distributed algorithms with strong cor- rectness and safety guarantees. However, such algorithms have failed to accom- pany, for theoretical and practical reasons, the requirements of the distributed systems they support in terms of scale, scope and pervasiveness. Reality is unfor- giving and thus researchers had the need to design and develop new algorithms based on probabilistic principles that, despite their probabilistic yet quantifiable guarantees, are suitable to today{\textquoteright}s modern distributed systems. In this dissertation, we study the challenges of and propose solutions for, ap- plying probabilistic dissemination algorithms, also known as epidemic- or gossip- based, in very large scale distributed systems. In particular, we focus on the issues of scalability of content types (topic-based publish-subscribe), content size (efficient data dissemination) and ordering requirements (total order). For each one of these issues, we present a novel distributed algorithm that solves the prob- lem while matching state-of-the art performance and trade-offs, and evaluate it on a realistic setting.</p>
},
	attachments = {https://haslab.uminho.pt/sites/default/files/mmatos/files/miguel_angelo_marques_de_matos.pdf},
	author = {Miguel Matos}
}