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Titlebook: Managing Distributed Cloud Applications and Infrastructure; A Self-Optimising Ap Theo Lynn,John G. Mooney,Keith A. Ellis Book‘‘‘‘‘‘‘‘ 2020

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发表于 2025-3-21 16:12:03 | 显示全部楼层 |阅读模式
书目名称Managing Distributed Cloud Applications and Infrastructure
副标题A Self-Optimising Ap
编辑Theo Lynn,John G. Mooney,Keith A. Ellis
视频video
概述Explores the use of novel technologies in reliable capacity provisioning across distributed clouds.Presents a state-of-the-art approach and reference model for reliable capacity provisioning in distri
丛书名称Palgrave Studies in Digital Business & Enabling Technologies
图书封面Titlebook: Managing Distributed Cloud Applications and Infrastructure; A Self-Optimising Ap Theo Lynn,John G. Mooney,Keith A. Ellis Book‘‘‘‘‘‘‘‘ 2020
描述.The emergence of the Internet of Things (IoT), combined with greater heterogeneity not only online in cloud computing architectures but across the cloud-to-edge continuum, is introducing new challenges for managing applications and infrastructure across this continuum. The scale and complexity is simply so complex that it is no longer realistic for IT teams to manually foresee the potential issues and manage the dynamism and dependencies across an increasing inter-dependent chain of service provision. .This Open Access Pivot explores these challenges and offers a solution for the intelligent and reliable management of physical infrastructure and the optimal placement of applications for the provision of services on distributed clouds. This book provides a conceptual reference model for reliable capacity provisioning for distributed clouds and discusses how data analytics and machine learning, application and infrastructure optimization, and simulation can deliver qualityof service requirements cost-efficiently in this complex feature space. These are illustrated through a series of case studies in cloud computing, telecommunications, big data analytics, and smart cities. ...
出版日期Book‘‘‘‘‘‘‘‘ 2020
关键词Analytics Models; Data Acquisition; Application Optimisation; Infrastructure; Distributed Clouds; digital
版次1
doihttps://doi.org/10.1007/978-3-030-39863-7
isbn_ebook978-3-030-39863-7Series ISSN 2662-1282 Series E-ISSN 2662-1290
issn_series 2662-1282
copyrightThe Editor(s) (if applicable) and The Author(s) 2020
The information of publication is updating

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Application Optimisation: Workload Prediction and Autonomous Autoscaling of Distributed Cloud Applils that build on them. Contributions in modelling, characterisation, and autoscaling of applications, as well as prediction and generation of workloads, are presented and discussed in the context of optimisation of distributed cloud applications operating in complex heterogeneous resource environments.
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RECAP Data Acquisition and Analytics Methodology,chine learning models from this data. These models are then used to identify relevant features and forecast future values, and thus inform run-time planning, decision making, and optimisation support at both the infrastructure and application levels. We conclude the chapter with an overview of RECAP data visualisation approaches.
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Towards an Architecture for Reliable Capacity Provisioning for Distributed Clouds,s. In addition, the major design concepts informing its design—namely separation of concerns, model-centricism, modular design, and machine learning and artificial intelligence for IT operations—are also discussed.
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RECAP Data Acquisition and Analytics Methodology,nfrastructure for the acquisition and processing of data from applications and systems, and explains the methodology used to derive statistical and machine learning models from this data. These models are then used to identify relevant features and forecast future values, and thus inform run-time pl
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Application Optimisation: Workload Prediction and Autonomous Autoscaling of Distributed Cloud Appliinfrastructure and application topologies, workload arrival and propagation patterns, and the predictability and variations of user behaviour. This chapter outlines the RECAP approach to application optimisation and presents its framework for joint modelling of applications, workloads, and the propa
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