Managing Distributed Cloud Applications and Infrastructure: A Self-Optimising Approach

ISBN-13: 9783030398620 / Angielski / Twarda / 2020 / 163 str.

Professor Theo Lynn; John G. Mooney; Jorg Domaschka

Spis treści

Chapter 1 - Towards an Architecture for Reliable Capacity Provisioning for Distributed Clouds

Chapter 2 - RECAP Data Acquisition and Analytics Methodology

Chapter 3 - Application Optimisation: Workload Prediction and Autonomous Autoscaling of Distributed Cloud Applications

Chapter 4 - Application Placement and Infrastructure Optimisation

Chapter 5 - Simulating Across the Cloud-to-Edge Continuum

Chapter 6 - Case Studies in Application Placement and Infrastructure Optimisation.

 

O autorze

Theo Lynn is Full Professor of Digital Business at DCU Business School, Ireland and Director of the Irish Institute of Digital Business.

John G. Mooney is Associate Professor of Information Systems and Technology Management at the Pepperdine Graziadio Business School, United States.

Jörg Domaschka is Senior Researcher and Group Manager at the Institute of Information Resource Management at Ulm University, Germany and Coordinator of the Horizon 2020 RECAP project.

Keith A. Ellis is Senior Research Scientist and Manager of Intel Labs Europe, Ireland.

Blurb

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 quality of 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.