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  • 1.
    Le Duc, Thang
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    García Leiva, Rafael
    Casari, Paolo
    Östberg, Per-Olov
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Machine Learning Methods for Reliable Resource Provisioning in Edge-Cloud Computing: A Survey2019In: ACM Computing Surveys, ISSN 0360-0300, E-ISSN 1557-7341, Vol. 52, no 5, article id 94Article in journal (Refereed)
    Abstract [en]

    Large-scale software systems are currently designed as distributed entities and deployed in cloud data centers. To overcome the limitations inherent to this type of deployment, applications are increasingly being supplemented with components instantiated closer to the edges of networks—a paradigm known as edge computing. The problem of how to efficiently orchestrate combined edge-cloud applications is, however, incompletely understood, and a wide range of techniques for resource and application management are currently in use.

    This article investigates the problem of reliable resource provisioning in joint edge-cloud environments, and surveys technologies, mechanisms, and methods that can be used to improve the reliability of distributed applications in diverse and heterogeneous network environments. Due to the complexity of the problem, special emphasis is placed on solutions to the characterization, management, and control of complex distributed applications using machine learning approaches. The survey is structured around a decomposition of the reliable resource provisioning problem into three categories of techniques: workload characterization and prediction, component placement and system consolidation, and application elasticity and remediation. Survey results are presented along with a problem-oriented discussion of the state-of-the-art. A summary of identified challenges and an outline of future research directions are presented to conclude the article.

  • 2.
    Le Duc, Thang
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Östberg, Per-Olov
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Application, Workload, and Infrastructure Models for Virtualized Content Delivery Networks Deployed in Edge Computing Environments2018In: 2018 27th International Conference on Computer Communication and Networks (ICCCN), IEEE, 2018Conference paper (Refereed)
    Abstract [en]

    Content Delivery Networks (CDNs) are handling a large part of the traffic over the Internet and are of growing importance for management and operation of coming generations of data intensive applications. This paper addresses modeling and scaling of content-oriented applications, and presents workload, application, and infrastructure models developed in collaboration with a large-scale CDN operating infrastructure provider aimed to improve the performance of content delivery subsystems deployed in wide area networks. It has been shown that leveraging edge resources for the deployment of caches of content greatly benefits CDNs. Therefore, the models are described from an edge computing perspective and intended to be integrated in network topology aware application orchestration and resource management systems.

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  • modern-language-association-8th-edition
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