Cloud Computing Principles Systems and Applications 2nd Edition by Nick Antonopoulos Lee Gillam 9783319546452 3319546457

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• ISBN 10:3319546457 

• ISBN 13: 9783319546452

• Author:Nick Antonopoulos, Lee Gillam 

Cloud Computing
Principles, Systems and Applications
Cloud computing has recently emerged as a subject of substantial industrial and academic interest, though its meaning and scope is hotly debated. For some researchers, clouds are a natural evolution towards the full commercialisation of grid systems, while others dismiss the term as a mere re-branding of existing pay-per-use technologies. From either perspective, “cloud” is now the label of choice for accountable pay-per-use access to third party applications and computational resources on a massive scale. Clouds support patterns of less predictable resource use for applications and services across the IT spectrum, from online office applications to high-throughput transactional services and high-performance computations involving substantial quantities of processing cycles and storage. The concept of clouds seems to blur the distinctions between a variety of technologies that encompass grid services, web services and data centres, and leads to considerations of lowered-cost provisioning for bursty applications. This book provides comprehensive coverage of the state of the art in cloud computing, highlighting and clarifying the conceptual and systemic links with other distributed computing approaches.

Cloud Computing Principles Systems and Applications 2nd Table of contents:

Part I General Principles
1 The Rise of Cloud Computing in the Era of Emerging Networked Society
1.1 Introduction
1.2 Cloud Computing in Nutshell
1.2.1 Service Models and Deployment Modes of Cloud Computing
1.2.1.1 Cloud Service Models
1.2.1.2 Cloud Deployment Modes
1.3 Networked Society
1.3.1 Taxonomy of Enabling Technologies of Networked Society
1.3.1.1 Edge Computing: Cloudlet, Fog Computing, and Mobile-Edge Computing
1.3.1.2 Internet of Things: Smart Grids and Smart Cities
1.3.1.3 Big Data
1.3.2 5G Networks: Technology Requirements and Potential Use Cases
1.3.2.1 Cloud Radio Access Network (C-RAN)
1.3.2.2 Tactile Internet
1.3.2.3 Software-Defined Networking (SDN)
1.3.2.4 Network Function Virtualization (NFV)
1.3.2.5 Augmented Reality, Virtual Reality, and Mixed Reality
1.3.2.6 Network Slicing
1.3.2.7 Containerization
1.4 Conclusions
References
2 Mirror Mirror on the Wall, How Do I Dimension My Cloud After All?
2.1 Introduction
2.2 Desiderata for HPC Applications and Scientific Workflows
2.2.1 Scientific Applications
2.2.2 Computer-Based Scientific Experiments
2.2.3 Scientific Workflows
2.3 Static Cloud Dimensioning
2.3.1 Mathematical Formulation
2.3.2 Federated Clouds Scenario
2.3.3 A Heuristic Approach
2.3.4 Experimental Results for Static Cloud Dimensioning
2.4 Dynamic Cloud Dimensioning
2.5 Survey on Existing Approaches for Cloud Dimensioning
2.6 Conclusions and Open Problems
References
3 A Taxonomy of Adaptive Resource Management Mechanisms in Virtual Machines: Recent Progress and Cha
3.1 Introduction
3.2 From Virtual Machines Fundamentals to Recent Trends
3.2.1 Computation as a Resource
3.2.2 Memory as a Resource
3.2.3 Input/Output as a Resource
3.2.4 Research Trends
3.3 Adaptation Techniques
3.3.1 System Virtual Machine
3.3.1.1 CPU Management
3.3.1.2 Memory Management
3.3.2 High-Level Language Virtual Machine
3.3.2.1 Just in Time Compilation
3.3.2.2 Garbage Collection
3.3.2.3 Resource Management
3.3.3 Summary of Techniques
3.4 The RCI Taxonomy
3.4.1 Quantitative Criteria of the RCI Taxonomy
3.4.2 Classification of Techniques
3.4.3 Aggregation of Quantities
3.4.4 Critical Analysis of the Taxonomy
3.5 VM Systems and Their Classification
3.5.1 System Virtual Machine
3.5.1.1 Friendly Virtual Machines (FVM)
3.5.1.2 ASMan
3.5.1.3 HPC Computing
3.5.1.4 Auto Control
3.5.1.5 PRESS
3.5.1.6 Overbooking and Consolidation
3.5.1.7 Difference Engine
3.5.1.8 VMMB
3.5.1.9 Overall System Analysis
3.5.2 High-Level Language Virtual Machines
3.5.2.1 KaffeOS
3.5.2.2 JRES
3.5.2.3 Multitask Virtual Machine (MVM)
3.5.2.4 Isla Vista
3.5.2.5 GC Switch
3.5.2.6 Paging-Aware GC
3.5.2.7 GC Economics
3.5.2.8 Control Theory
3.5.2.9 Machine Learning for Memory Management
3.5.2.10 Overall Systems Analysis
3.6 Summary and Open Research Issues
References
Part II Science Cloud
4 Exploring Cloud Elasticity in Scientific Applications
4.1 Introduction
4.2 Basic Concepts and State of the Art
4.2.1 Taxonomy and Classification
4.2.2 Elasticity in Scientific Applications
4.3 Developing Elastic Scientific Applications
4.3.1 Programming Level Elasticity
4.3.1.1 Architecture
4.3.2 Middleware Level Elasticity
4.3.2.1 Architecture
4.3.2.2 Model of Parallel Application
4.4 Elasticity Analysis and Research Opportunities
4.5 Conclusion
References
5 Clouds and Reproducibility: A Way to Go to ScientificExperiments?
5.1 Introduction
5.2 A Taxonomy on Reproducibility of Experiments
5.3 How Clouds Can Foster Reproducibility in Science?
5.4 Reproducible Research Architecture
5.5 Survey on Approaches for Reproducible Science
5.5.1 SHARE: Sharing Hosted Autonomous Research Environments
5.5.2 Paper Mâché
5.5.3 CDE: Code, Data, and Environment
5.5.4 Reprozip
5.5.5 PASS: Provenance Aware Storage Systems
5.5.6 SciCumulus Workflow System
5.5.7 Reproducible Research in the Cloud
5.5.7.1 WSSE: Whole System Snapshot Exchange
5.5.7.2 Chef
5.5.7.3 Reproducibility with AMOS
5.5.7.4 PDIFF: Using Provenance and Data Differencing for Workflow Reproducibility
5.5.8 Final Considerations
5.6 Conclusions
References
6 Big Data Analytics in Healthcare: A Cloud-Based Framework for Generating Insights
6.1 Introduction
6.2 Genomics and Clinical Data
6.2.1 Genomics Data
6.2.2 Clinical Data
6.3 Data Integration
6.4 Data Consistency
6.5 Data Infrastructure
6.6 Data Analysis
6.7 Conclusions
References
Part III Data Cloud
7 High-Performance Graph Data Management and Mining in Cloud Environments with X10
7.1 Introduction
7.2 Challenges and Technologies: Review of Previous Work
7.2.1 HPC Graph Data Processing
7.2.2 Graph Data Management
7.2.3 HPC Graph Data Management Benchmarks
7.3 Overview of X10
7.4 Large Graph Processing with X10
7.4.1 ScaleGraph Architecture
7.4.2 Implementation of Graph Algorithms in ScaleGraph
7.4.2.1 Degree Distribution Calculation
7.4.2.2 Betweenness Centrality
7.4.2.3 Spectral Clustering
7.5 X10-Based Distributed Graph Database Engine
7.5.1 System Design
7.5.2 Implementation of Acacia
7.5.3 RDF Data Partitioner and Native Store
7.5.4 SPARQL Query Processor
7.5.5 Evaluation of Acacia’s Performance
7.6 XGDBench Graph Database Benchmarking Framework on Clouds
7.6.1 Methodology of XGDBench
7.6.2 Requirements of XGDBench
7.6.2.1 Attribute Read/Update
7.6.2.2 Graph Traversal
7.6.3 Implementation of XGDBench
7.6.3.1 Graph Generator
7.6.3.2 Graph Data Structure
7.6.3.3 Workload Executor
7.6.3.4 Graph DB Workload
7.6.3.5 Graph DB Interface Layer
7.6.3.6 Implementation of Traversal Operation
7.6.3.7 Implementation of Insert and Update Operations
7.6.4 Evaluation of XGDBench in HPC Cluster
7.6.4.1 Performance Evaluation of Titan
7.6.4.2 Evaluation of Graph Generation Time
7.7 Conclusion
References
8 Implementing MapReduce Applications in Dynamic Cloud Environments
8.1 Introduction
8.2 MapReduce Background
8.3 P2P-MapReduce Architecture
8.4 System Mechanisms
8.5 Implementation
8.6 Evaluation
8.7 Conclusions
References
Part IV Multi-clouds
9 Facilitating Cloud Federation Management via DataInteroperability
9.1 Introduction
9.2 Challenges and Related Work
9.2.1 Challenges to Cloud Federation Deployment
9.2.2 Related Work
9.3 Cloud Service Monitoring
9.3.1 Architecture Design
9.3.2 Implementation
9.4 Data Interchange Formats
9.4.1 eXtensible Markup Language
9.4.2 JavaScript Object Notation
9.4.3 MessagePack
9.4.4 Protocol Buffers
9.5 Messaging Bus Communication System
9.5.1 Intercommunication Potential
9.5.2 Design and Implementation
9.5.2.1 Producer
9.5.2.2 Messaging Infrastructure
9.5.2.3 Consumer
9.6 Cloud Federation Management
9.6.1 Architecture Design
9.6.2 Architecture Importance for Cloud Advancement
9.7 Data Interchange Format and Message Bus Evaluations
9.7.1 Evaluation Environment Setup
9.7.2 Use Case Description
9.7.3 Data Structuring
9.7.3.1 XML
9.7.3.2 JSON
9.7.3.3 MessagePack
9.7.3.4 Protocol Buffers
9.7.4 Serialisation Compactness
9.7.4.1 Short Summary
9.8 Conclusion and Future Work
References
10 Applying Self-* Principles in Heterogeneous Cloud Environments
10.1 Introduction
10.2 Autonomic Computing
10.2.1 Properties of Autonomic Computing
10.2.2 The Autonomic Loop
10.2.3 European Initiatives for Autonomic Clouds
10.3 Cloud Architectures
10.3.1 Service Automation
10.3.2 Autonomic SLA Management
10.3.3 Cloud Brokerage and Cloud Service Lifecycle
10.4 Self-*
10.5 Applications of Self-* Principles in Cloud Computing
10.6 Conclusion
References
Part V Performance and Efficiency
11 Optimizing the Profit and QoS of Virtual Brokers in the Cloud
11.1 Introduction
11.2 Brokering and Virtual Brokering in Cloud Computing Systems
11.2.1 Cloud Brokering
11.2.2 Broker Types
11.2.3 The Virtual Broker for IaaS
11.3 Virtual Machine Planning for a Virtual Cloud Broker
11.3.1 Problem Formulation
11.3.2 Extended Problem Formulation
11.4 The Proposed Scheduling Methods
11.4.1 Online Scheduling Heuristics
11.4.2 Offline Scheduling Heuristics
11.5 Experimental Evaluation
11.5.1 Problem Instances
11.5.2 Computing Infrastructure
11.5.3 Experimental Results for the Location-Agnostic Problem
11.5.4 Experimental Results for the Location-Aware Problem
11.6 Conclusions and Future Work
References
12 Adaptive Resource Allocation for Load Balancing in Cloud
12.1 Introduction
12.2 Related Work
12.3 Cloud Computing Continuum
12.3.1 Cloudlets
12.3.2 Fog Computing
12.3.3 Cloud-IoT
12.4 Cloud Hardware Resources
12.5 Workload Management
12.5.1 Load Balancing Techniques
12.5.2 Existing Proactive Measures
12.6 PRAS: Proactive Resource Allocation Strategy
12.6.1 Prediction
12.6.1.1 ARIMA
12.6.1.2 SARIMA: Seasonal ARIMA
12.6.1.3 ARFIMA
12.6.2 Particle Swarm Optimization-Based Scheduling
12.6.2.1 VM Allocation
12.7 Evaluation
12.7.1 Prediction Results
12.7.1.1 PSO Results
12.8 Conclusions and Future Work
References
13 Datacentre Event Analysis for Knowledge Discovery in Large-Scale Cloud Environments
13.1 Introduction
13.2 Cloud Workload Analytics
13.3 Cloud Predictability
13.4 Datacentre Trace Sample
13.5 Submission Event Analysis
13.6 Machine Usage Analysis
13.6.1 Machine Events
13.6.2 Machine Usage Frequency Analysis
13.7 Resource Request Analysis
13.8 Conclusion
References
14 Cloud-Supported Certification for Energy-Efficient Web Browsing and Services
14.1 Introduction
14.2 Related Work
14.2.1 Dynamic Power Management
14.2.1.1 Classification of Dynamic Power Management Systems
14.2.1.2 Relevant Dynamic Power Management Solutions
14.2.2 Energy-Aware Scheduling Systems
14.2.3 Energy-Related Certification and Analytics on the Cloud
14.2.3.1 Energy-Related Certification Computational Systems
14.2.3.2 Classes of Big Data Analytic System
14.2.3.3 Relevant Energy-Related Big Data Analytic Systems
14.2.4 Analysis and Discussion
14.3 An Architecture for Energy-Efficient Browsing
14.3.1 Browser Extension and Power Management
14.3.1.1 Browser-Level Management Policies
14.3.1.2 Tab Management Mechanisms
14.3.1.3 Enforcing Limits
14.3.2 Certification Back End
14.3.2.1 Performance Counters for Energy-Related Certification
14.3.2.2 Devising Categories and Certifying Pages
14.4 Browser-Level Extensions and Certification Back End
14.4.1 Browser Extension
14.4.2 Certification Back End
14.5 Evaluation
14.5.1 Resource Usage Evaluation
14.5.2 Perceived Delay Evaluation
14.6 Conclusions
References
Author Index
Subject Index

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