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Open Access 2020 | Open Access | Book

Cover of the book

Managing Distributed Cloud Applications and Infrastructure

A Self-Optimising Approach

Editors: Theo Lynn, Prof. John G. Mooney, Dr. Jörg Domaschka, Keith A. Ellis

Publisher: Springer International Publishing

Book Series : Palgrave Studies in Digital Business & Enabling Technologies

Part of: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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About this 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 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.

Table of Contents

Frontmatter
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Open Access

Chapter 1. Towards an Architecture for Reliable Capacity Provisioning for Distributed Clouds
Abstract
The complexity of computing along the cloud-to-edge continuum presents significant challenges to ICT operations and in particular reliable capacity planning and resource provisioning to meet unpredictable, fluctuating, and mobile demand. This chapter presents a high-level conceptual overview of RECAP—an architectural innovation to support reliable capacity provisioning for distributed clouds—and its operational modes and functional building blocks. 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.
Jörg Domaschka, Frank Griesinger, Mark Leznik, Per-Olov Östberg, Keith A. Ellis, Paolo Casari, Frank Fowley, Theo Lynn
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Open Access

Chapter 2. RECAP Data Acquisition and Analytics Methodology
Abstract
The collection, analysis, and processing of infrastructure information and telemetry data lie at the very heart of RECAP. This chapter describes the infrastructure 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 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.
Paolo Casari, Jörg Domaschka, Rafael García Leiva, Thang Le Duc, Mark Leznik, Linus Närvä
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Open Access

Chapter 3. Application Optimisation: Workload Prediction and Autonomous Autoscaling of Distributed Cloud Applications
Abstract
Optimisation of (the configuration and deployment of) distributed cloud applications is a complex problem that requires understanding factors such as infrastructure 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 propagation of workloads in applications and networks. The interaction of the models and algorithms developed is described and presented along with the tools 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.
Per-Olov Östberg, Thang Le Duc, Paolo Casari, Rafael García Leiva, Antonio Fernández Anta, Jörg Domaschka
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Open Access

Chapter 4. Application Placement and Infrastructure Optimisation
Abstract
This chapter introduces the RECAP Infrastructure Optimiser tasked with optimal application placement and infrastructure optimisation. The chapter details the methodology, models, and algorithmic approach taken to augment the RECAP Application Optimiser output in producing a more holistic optimisation, cognisant of both application and infrastructure provider interests.
Radhika Loomba, Keith A. Ellis
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Open Access

Chapter 5. Simulating Across the Cloud-to-Edge Continuum
Abstract
As growth and adoption of the Internet of Things continue to accelerate, cloud infrastructure and communication service providers (CSPs) need to assure the efficient performance of their services while meeting the Quality of Service (QoS) requirements of their customers and their end users, while maintaining or ideally reducing costs. To do this, testing and service quality assurance are essential. Notwithstanding this, the size and complexity of modern infrastructures make real-time testing and experimentation difficult, time-consuming, and costly. The RECAP Simulation Framework offers cloud and communication service providers an alternative solution while retaining accuracy and verisimilitude. It comprises two simulation approaches, Discrete Event Simulation (DES) and Discrete Time Simulation (DTS). It provides information about optimal virtual cache placements, resource handling and remediation of the system, optimal request servicing, and finally, optimal distribution of requests and resource adjustment, with the goal to increase performance and concurrently decrease power consumption of the system.
Minas Spanopoulos-Karalexidis, Christos K. Filelis Papadopoulos, Konstantinos M. Giannoutakis, George A. Gravvanis, Dimitrios Tzovaras, Malika Bendechache, Sergej Svorobej, Patricia Takako Endo, Theo Lynn
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Open Access

Chapter 6. Case Studies in Application Placement and Infrastructure Optimisation
Abstract
This chapter presents four case studies each illustrating an implementation of one or more RECAP subsystems. The first case study illustrates how RECAP can be used for infrastructure optimisation for a 5G network use case. The second case study explores application optimisation for virtual content distribution networks (vCDN) on a large Tier 1 network operator. The third case study looks at how RECAP components can be embedded in an IoT platform to reduce costs and increase quality of service. The final case study presents how data analytics and simulation components, within RECAP, can be used by a small-to-medium-sized enterprise (SME) for cloud capacity planning.
Miguel Angel López-Peña, Hector Humanes, Johan Forsman, Thang Le Duc, Peter Willis, Manuel Noya
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Backmatter
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Metadata
Title
Managing Distributed Cloud Applications and Infrastructure
Editors
Theo Lynn
Prof. John G. Mooney
Dr. Jörg Domaschka
Keith A. Ellis
Copyright Year
2020
Electronic ISBN
978-3-030-39863-7
Print ISBN
978-3-030-39862-0
DOI
https://doi.org/10.1007/978-3-030-39863-7

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