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

Orthosis

書目名稱Managing Distributed Cloud Applications and Infrastructure影響因子(影響力)




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書目名稱Managing Distributed Cloud Applications and Infrastructure網(wǎng)絡公開度




書目名稱Managing Distributed Cloud Applications and Infrastructure網(wǎng)絡公開度學科排名




書目名稱Managing Distributed Cloud Applications and Infrastructure被引頻次




書目名稱Managing Distributed Cloud Applications and Infrastructure被引頻次學科排名




書目名稱Managing Distributed Cloud Applications and Infrastructure年度引用




書目名稱Managing Distributed Cloud Applications and Infrastructure年度引用學科排名




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磨坊

geometrician

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.

Itinerant

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.

分開如此和諧

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.

stratum-corneum

inspired

星星

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

態(tài)度暖昧

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

HARP