2,694 research outputs found
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
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
Robust and efficient membership management in large-scale dynamic networks
Epidemic protocols are a bio-inspired communication and computation paradigm for large-scale networked systems based on randomised communication. These protocols rely on a membership service to build decentralised and random overlay topologies. In large-scale, dynamic network environments, node churn and failures may have a detrimental effect on the structure of the overlay topologies with negative impact on the efficiency and the accuracy of applications. Most importantly, there exists the risk of a permanent loss of global connectivity that would prevent the correct convergence of applications. This work investigates to what extent a dynamic network environment may negatively affect the performance of Epidemic membership protocols. A novel Enhanced Expander Membership Protocol (EMP+) based on the expansion properties of graphs is presented. The proposed protocol is evaluated against other membership protocols and the comparative analysis shows that EMP+ can support faster application convergence and is the first membership protocol to provide robustness against global network connectivity problems
Global inequality: a multidimensional perspective
This paper investigates the evolution of global well-being inequality between 1980 and 2010 based on three dimensions: income, health and education. The inequality of each of these dimensions shows a different pattern over time. To make an overall assessment of the evolution of well-being inequality, I make use of a recently developed multidimensional inequality index which reflects the implicit value judgments of the revised Human Development Index. Multidimensional well-being inequality has decreased over the considered period. However, this result is shown to depend crucially on the weighting scheme selected, the aggregation procedure and the transformation of the income dimension.
Agreement in epidemic information dissemination
Consensus is one of the fundamental problems in multi-agent systems and distributed computing, in which agents or processing nodes are required to reach global agreement on some data value, decision, action, or synchronisation. In the absence of centralised coordination, achieving global consensus is challenging especially in dynamic and large-scale distributed systems with faulty processes. This paper presents a fully decentralised phase transition protocol to achieve global consensus on the convergence of an underlying information dissemination process. The proposed approach is based on Epidemic protocols, which are a randomised communication and computation paradigm and provide excellent scalability and fault-tolerant properties. The experimental analysis is based on simulations of a large-scale information dissemination process and the results show that global agreement can be achieved without deterministic and global communication patterns, such as those based on centralised coordination
Formal analysis techniques for gossiping protocols
We give a survey of formal verification techniques that can be used to corroborate existing experimental results for gossiping protocols in a rigorous manner. We present properties of interest for gossiping protocols and discuss how various formal evaluation techniques can be employed to predict them
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