TY  - BOOK
AU  - Lynn,Theo
AU  - Mooney,John G.
AU  - Domaschka,Jörg
AU  - Ellis,Keith A.
ED  - SpringerLink (Online service)
TI  - Managing Distributed Cloud Applications and Infrastructure: A Self-Optimising Approach 
T2  - Palgrave Studies in Digital Business & Enabling Technologies,
SN  - 9783030398637
AV  - HD45 
U1  - 658.4062 23
PY  - 2020///
CY  - Cham
PB  - Springer International Publishing, Imprint: Palgrave Macmillan
KW  - Technological innovations
KW  - Electronic commerce
KW  - Computer engineering
KW  - Computer networks 
KW  - Innovation and Technology Management
KW  - e-Commerce and e-Business
KW  - Computer Engineering and Networks
N1  - 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; Open Access
N2  - 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.
UR  - https://doi.org/10.1007/978-3-030-39863-7
ER  -