Transferable knowledge for Low-cost Decision Making in Cloud Environments

SAMREEN, Faiza, BLAIR, Gordon and ELKHATIB, Yahia (2020). Transferable knowledge for Low-cost Decision Making in Cloud Environments. IEEE Transactions on Cloud Computing.

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Official URL: https://ieeexplore.ieee.org/document/9097348
Link to published version:: https://doi.org/10.1109/TCC.2020.2989381
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    Abstract

    Users of Infrastructure as a Service (IaaS) are increasingly overwhelmed with the wide range of providers and services offered by each provider. As such, many users select services based on description alone. An emerging alternative is to use a decision support system (DSS), which typically relies on gaining insights from observational data in order to assist a customer in making decisions regarding optimal deployment of cloud applications. The primary activity of such systems is the generation of a prediction model (e.g. using machine learning), which requires a significantly large amount of training data. However, considering the varying architectures of applications, cloud providers, and cloud offerings, this activity is not sustainable as it incurs additional time and cost to collect data to train the models. We overcome this through developing a Transfer Learning (TL) approach where knowledge (in the form of a prediction model and associated data set) gained from running an application on a particular IaaS is transferred in order to substantially reduce the overhead of building new models for the performance of new applications and/or cloud infrastructures. In this paper, we present our approach and evaluate it through extensive experimentation involving three real world applications over two major public cloud providers, namely Amazon and Google. Our evaluation shows that our novel two-mode TL scheme increases overall efficiency with a factor of 60% reduction in the time and cost of generating a new prediction model. We test this under a number of cross-application and cross-cloud scenarios

    Item Type: Article
    Additional Information: © 2020 IEEE.  Personal use of this material is permitted.  Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Uncontrolled Keywords: 0805 Distributed Computing; 0806 Information Systems
    Identification Number: https://doi.org/10.1109/TCC.2020.2989381
    SWORD Depositor: Symplectic Elements
    Depositing User: Symplectic Elements
    Date Deposited: 01 May 2020 10:57
    Last Modified: 18 Jun 2020 19:57
    URI: http://shura.shu.ac.uk/id/eprint/26220

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