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Stochastic models for resource allocation in large distributed systems

Abstract : This PhD thesis investigates four problems in the context of Large Distributed Systems. This work is motivated by the questions arising with the expansion of Cloud Computing and related technologies. The present work investigates the efficiency of different resource allocation algorithms in this framework. The methods used involve a mathematical analysis of several stochastic models associated to these networks. Chapter 1 provides an introduction to the subject in general, as well as a presentation of the main mathematical tools used throughout the subsequent chapters. Chapter 2 presents a congestion control mechanism in Video on Demand services delivering files encoded in various resolutions. We propose a policy under which the server delivers the video only at minimal bit rate when the occupancy rate of the server is above a certain threshold. The performance of the system under this policy is then evaluated based on both the rejection and degradation rates. Chapters 3, 4 and 5 explore problems related to cooperation schemes between data centres on the edge of the network. In the first setting, we analyse a policy in the context of multi-resource cloud services. In second case, requests that arrive at a congested data centre are forwarded to a neighbouring data centre with some given probability. In the third case, requests blocked at one data centre are forwarded systematically to another where a trunk reservation policy is introduced such that a redirected request is accepted only if there are a certain minimum number of free servers at this data centre.
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Submitted on : Monday, October 8, 2018 - 2:04:05 PM
Last modification on : Wednesday, October 14, 2020 - 1:49:16 PM


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  • HAL Id : tel-01661815, version 2


Guilherme Thompson. Stochastic models for resource allocation in large distributed systems. Numerical Analysis [math.NA]. Université Pierre et Marie Curie - Paris VI, 2017. English. ⟨NNT : 2017PA066539⟩. ⟨tel-01661815v2⟩



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