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    Adaptive resource provisioning in cloud computing environments

    Chouliaras, Spyridon (2023) Adaptive resource provisioning in cloud computing environments. PhD thesis, Birkbeck, University of London.

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    Abstract

    Cloud computing emerged as a technology that offers scalable access to computing resources in conjunction with low maintenance costs. In this domain, cloud users utilize virtualized resources to benefit from elastic computing and efficient pricing strategies. Although, cloud users have access to large amount of resources, it is yet a challenging task to efficiently manage the resources in cloud computing environments. In that context, cloud providers offer auto-scaling services that need to be configured by the users according to application requirements. Still, tuning scaling parameters is not trivial, since it is mainly based on static scaling rules that may lead to unreasonable costs and quality of service violations. This thesis introduces a reliable adaptive resource provisioning framework for database applications in cloud computing environments. The framework is organized around three main services to enable a) anomaly detection for reliable decision making, b) resource provisioning for effective and efficient workload execution and c) constrained optimization to identify the optimal configurations that maximize application performance based on user requirements. The services included in this thesis utilize a variety of artificial intelligence techniques including Artificial Neural Networks for supervised learning, K-means for unsupervised learning and Genetic Algorithms for constrained optimization. The proposed techniques are based on monitored metrics collected from the running systems deployed in the cloud. Finally, this thesis presents an extended experimental analysis using industry standard applications and state-of-the-art benchmarks to demonstrate the robustness and effectiveness of the proposed framework.

    Metadata

    Item Type: Thesis
    Additional Information: Date of award confirmed as 2023 by registry.
    Copyright Holders: The copyright of this thesis rests with the author, who asserts his/her right to be known as such according to the Copyright Designs and Patents Act 1988. No dealing with the thesis contrary to the copyright or moral rights of the author is permitted.
    Depositing User: Acquisitions And Metadata
    Date Deposited: 11 May 2023 15:00
    Last Modified: 01 Nov 2023 16:10
    URI: https://eprints.bbk.ac.uk/id/eprint/51213
    DOI: https://doi.org/10.18743/PUB.00051213

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