Managing Distributed Cloud Applications and Infrastructure
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. 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.
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description_of_book
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 challenge
معلومات إضافية:
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تاريخ الإصدار
03 أغسطس 2020
ردمك -الرقم الدولي المعياري للكتب-
978-3-030-39863-7
عن المؤلفين:
Theo Lynn(editor)
John G. Mooney(editor)
Institute of Information Resource Management Ulm University Ulm, Germany. Jörg Domaschka is a Senior Researcher and Group Manager at the Institute of Information Resource Management at Ulm University. He holds a Diploma in Computer Science from FAU, Erlangen Nuremberg, and a PhD in Computer Sscience from Ulm University. His research inter- ests include distributed systems, fault-tolerance, middleware platforms, and NoSQL databases. Current focus of his work lies on middleware and run-time systems for geo-distributed infrastructure and applications.
Jörg Domaschka (Editor)
Intel Labs Europe Dublin, Ireland. Keith A. Ellis is a Senior Research Scientist and Manager of Intel Labs Europe, Ireland. His research focus is orchestration and control in Cyber Physical Systems. Ellis has led and been actively involved with national, international, and European part-funded research— REVISITE, COOPERATE, IMR, WISDOM, RealValue, and EL4L, targeting various domains—built environment, industrial, water management, smart grid. and agricultural. He is the holder of mul- tiple patents and author of several journal articles, book chapters, and conference papers. He holds an MSc in Tech and Innovation Management and a BSc (Hons) in Tech.
Keith A. Ellis (Editor)
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