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Asynchronous epidemic algorithms for consistency in large-scale systems
Achieving and detecting a globally consistent state is essential to many services in the large
and extreme-scale distributed systems, especially when the desired consistent state is critical
for services operation. Centralised and deterministic approaches for synchronisation and
distributed consistency are not scalable and not fault-tolerant. Alternatively, epidemic-based
paradigms are decentralised computations based on randomised communications. They are
scalable, resilient, fault-tolerant, and converge to the desired target in logarithmic time with
respect to system size. Thus, many distributed services have adopted epidemic protocols
to achieve the consensus and the consistent state, mainly due to scalability concerns. The
convergence of epidemic protocols is stochastically guaranteed. However, the detection of
the convergence is probabilistic and non-explicit. In a real-world environment, systems are
unreliable, and epidemic protocols cannot converge to the desired state. Thus, achieving
convergence by itself does not ensure making a system-wide consistent state under dynamic
conditions.
The research work presented in this thesis introduces the Phase Transition Algorithm
(PTA) to achieve distributed consistent state based on the explicit detection of convergence.
Each phase in PTA is a decentralised decision-making process that implements epidemic data
aggregation, in which the detection of convergence implies achieving a global agreement. The
phases in PTA can be cascaded to achieve higher certainty as desired. Following the PTA,
two epidemic protocols, namely PTP and ECP, are proposed to acquire of consensus, i.e. for
the consistency in data dissemination and data aggregation. The protocols are examined
through simulations, and experimental results have validated the protocols ability to achieve
and explicitly detect the consensus among system nodes.
The research work has also studied the epidemic data aggregation under nodes churn and
network failures, in which the analysis has identified three phases of the aggregation process.
The investigations have shown a different impact of nodes churn on each phase. The phase
that is critical for the aggregation process has been studied further, which led to propose
new robust data aggregation protocols, REAP and REAP+. Each protocol has a different
decentralised replication method, and both implements distributed failure detection and
instantaneous mass restoration mechanisms. Simulations have validated the protocols, and
results have shown protocols ability to converge, detect convergence, and produce competitive
accuracy under various levels of nodes churn.
Furthermore, distributed consistency in continuous systems is addressed in the research.
The work has proposed a novel continuous epidemic protocol with the adaptive restart
mechanism. The protocol restarts either upon the detection of system convergence or upon
the detection of divergence. Also, the protocol introduces the seed selection method for
the peak data distribution in decentralised approaches, which was a challenge that requires
single-point initialisation and leader-election step. The simulations validated the performance
of the algorithm under static and dynamic conditions and approved that convergence and
divergence detection accuracy can be tuned as desired.
Finally, the research work shows that combining and integrating of the proposed protocols
enables extreme-scale distributed systems to achieve and detect global consistent states even
under realistic and dynamical conditions
Cloud Computing: caracterizaci贸n de los impactos positivos obtenidos por la utilizaci贸n del modelo Cloud Computing por las pymes, basado en la tipolog铆a de modelos de negocio de este tipo de empresas
[ES] El Cloud Computing produce importantes beneficios a las empresas usuarias, en especial a las
pymes. A trav茅s de 茅l estas empresas tienen mejor acceso a las tecnolog铆as de la informaci贸n que
necesitan para su funcionamiento. Seg煤n las estad铆sticas de utilizaci贸n del cloud computing, estas
empresas hacen un uso limitado de este tipo de servicios. El objetivo del presente trabajo es
contribuir a potenciar la utilizaci贸n del cloud por parte de las pymes.
Seg煤n nuestro diagn贸stico, el primer problema es del desconocimiento del cloud por parte de las
pymes. Para abordar este problema se realiza una descripci贸n del cloud computing y se analizan
los beneficios que les proporciona a las empresas usuarias. Para contribuir a convencer a los
empresarios de las ventajas que el uso del cloud les proporciona, se aborda el cloud desde una
贸ptica empresarial y para ello se propone un modelo de negocio tipo para las pymes, para
posteriormente relacionar los bloques en que se puede descomponer el citado modelo de negocio
con las tecnolog铆as de la informaci贸n y la comunicaci贸n adecuadas para el funcionamiento
de la empresa, accedidas a trav茅s del Cloud.Fons G贸mez, FJ. (2014). Cloud Computing: caracterizaci贸n de los impactos positivos obtenidos por la utilizaci贸n del modelo Cloud Computing por las pymes, basado en la tipolog铆a de Modelos de Negocio de este tipo de empresas. http://hdl.handle.net/10251/38864.Archivo delegad
Models and Algorithms for High-Performance Distributed Data Mining
1noThe problem of devising models and algorithms for high-performance Distributed Data Mining has traditionally been of great interest for the Data Mining and Database communities, merged with researchers and scientists from the Distributed Computing area. In addition to this well-known trend, the emerging MapReduce initiative has conferred a new light on research challenges posed by effectively and efficiently supporting Distributed Data Mining in high-performance environments.nonenoneCUZZOCREA ACuzzocrea, Alfredo Massimilian