Probabilistic Models of Partial Order Enforcement in Distributed Systems

Abstract : Distributed systems have managed to extend technology to a broader audience, in both terms of location and numbers. However these geo-replicated systems need to be scalable in order to meet the ever growing demands. Moreover, the system has to be able to process messages in an equivalent order that they were created to avoid unwanted side effects. Partial order enforcement provides an ordering of events that all nodes will follow therefore processing the messages in an adequate order. A system that enforces a partial order simplifies the challenge of developing distributed applications, and ensures that the end-user will not observe causality defying behaviors. In this thesis we present models for different partial order enforcements, using different latency model distributions. While a latency model, which yields the time it takes for a message to go from one node to another, our model builds on it to give the additional time that it takes to enforce a given partial order. We have proposed the following models. First, in a one to many nodes communication, the probability for the message to be delivered in all the nodes before a given time. Second, in a one to many nodes communication from the receivers, the probability that all the other nodes have delivered the message after a given time of him receiving it. Third, in a one to many nodes communication, the probability that the message has arrived to at least a subset of them before a given time. Fourth, applying either FIFO or Causal ordering determining if a message is ready for being delivered, in one node or many. All of this furthers the understanding of how distributed systems with partial orders behave. Furthermore using this knowledge we have built an algorithm that uses the insight of network behavior to provide a reliable causal delivery system. In order to validate our models, we developed a simulation tool that allows to run scenarios tailored to our needs. We can define the different parameters of the latency model, the number of clients, and the clients workloads. This simulation allows us to compare the randomly generated values for each specific configuration with the predicted outcome from our model. One of the applications that can take advantage of our model, is a reliable causal delivery algorithm. It uses causal information to detect missing elements and removes the need of message acknowledgment by contacting other replicas only when the message is assumed missing. This information is provided by our model, that defines waiting timers according to the network statistics and resource consumption. Finally this application has been both tested in the same simulator as the models, with promising results, and then evaluated in a real-life experiment using Amazon EC2 for the platform
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Submitted on : Monday, November 27, 2017 - 8:43:54 PM
Last modification on : Tuesday, December 18, 2018 - 4:26:02 PM


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  • HAL Id : tel-01649866, version 1


Jordi Martori Adrian. Probabilistic Models of Partial Order Enforcement in Distributed Systems. Distributed, Parallel, and Cluster Computing [cs.DC]. Université de Lorraine, 2017. English. ⟨NNT : 2017LORR0040⟩. ⟨tel-01649866⟩



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