Autonomic Computing in Cloud Resource Management in Industry 4.0

Joint Foreword
Preface
Acknowledgment
Contents
Introduction to Cloud Resource Management
	1 Introduction
	2 NIST's Cloud Model
	3 Benefits of the Cloud Model
	4 Cloud Computing Architecture
	5 Cloud Resource Management
	6 Industry 4.0
		6.1 Industry 4.0 Applications
	7 Significance of Cloud Computing in Industry 4.0
		7.1 Types of Distributed Cloud
		7.2 Benefits of Distributed Computing
		7.3 Challenges With Distributed Computing
	8 Use Cases for Cloud Computing in Industry 4.0
		8.1 The Next-Generation Services for Automotive Sector
		8.2 Localized Network With Distributed Computing
		8.3 5G Technology and Augmented Reality
		8.4 Distributed Cloud Solution by Ericcson
	9 Conclusion
	References
Emerging Paradigms and Practices in Cloud Resource Management
	1 Introduction
	2 Literature Review
		2.1 Cloud Resource Management
		2.2 Essential Concepts and Definitions
	3 Functions of Cloud Resource Management
		3.1 Cloud Recourse Provisioning
		3.2 Cloud Resource Scheduling
		3.3 Cloud Resource Monitoring
		3.4 Resource Management Techniques/Methods
		3.5 Service-Level Agreements (SLAs) Gaps in Cloud Computing
		3.6 The CSPs SLA Monitoring Mechanism (Table 1)
		3.7 Client-Centric SLA Framework for Enhancing the Trust, Transparency, and QoS
		3.8 The Client-Centric SLA Framework
	4 Experimental Analysis and Discussions
	5 Challenges in Data Centre Resource Management
	6 Conclusion
	7 Unanswered Questions as Recommendations for the Future Research Efforts
	References
Autonomic Computing in Cloud: Model and Applications
	1 Introduction
	2 Autonomic Computing Models
	3 IBM's Model for Autonomic Computing
		3.1 The Autonomic Manager
		3.2 Managed Element
		3.3 Sensors
		3.4 Effectors
	4 Challenges in Autonomic Computing
	5 Applications of Autonomic Systems
		5.1 Manufacturing Industry
		5.2 Automotive Industry
		5.3 Healthcare Management
		5.4 Robotics
	6 Conclusion
	References
Autonomic Computing: Models, Applications, and Brokerage
	1 Introduction
		1.1 Evolution of Autonomic Computing
	2 Self-Management Properties of the Autonomic Computing System
		2.1 The Defined Conditions for Autonomic System
		2.2 Fundamental Benefits of Autonomic Computing
		2.3 Future of Autonomic Computing
		2.4 Trust, Transparency, and QoS Assurances in Service-Level Agreements
	3 Building Block (Architecture) of Autonomic Computing
		3.1 The Basic Architecture of Autonomic Computing
		3.2 Autonomic Manager
	4 Autonomic Computing Models
		4.1 The MAPE-K Autonomic Loop Model
		4.2 The Role of Autonomic Manager and Cloud-TM
		4.3 MAPE-K Loop Deployment Using Autonomic Toolkit
		4.4 Monitoring in MAPE-K Loop
		4.5 Planning in MAPE-K Loop
		4.6 Knowledge in MAPE-K Loop
	5 Applications of Autonomic Computing
		5.1 Self-Healing Computing Systems and Communication Service Management
		5.2 Autonomic Computing in Traffic and Transportation System Management
		5.3 Autonomic Computing in Self-Driving Vehicle and Aircrafts
		5.4 Autonomic Computing in Virtualized Environment Management
		5.5 Autonomic Computing in Business Applications Management
		5.6 Autonomic Computing in E-Governance Applications
	6 Autonomic Model for Green Cloud Service Broker
		6.1 Simulated Experiment and Analysis Using CloudSim
		6.2 The Experimental Setup for Power-Aware Technique (DVFS Enable)
		6.3 Results Analysis
			6.3.1 Experimental Results Using Power-Aware Technique
			6.3.2 Experimental Results in Non-Power-Aware Technique
		6.4 Green Cloud Service Broker Model
	7 Conclusion
	References
Issues and Challenges in Autonomic Computing and Resource Management
	1 Introduction
		1.1 Classification of Autonomic Systems
	2 Challenges Due to Heterogeneity in Cloud Computing
	3 Challenges in Autonomic Computing
		3.1 Computing Challenges Due to COVID-19
		3.2 Cloud Adoption Strategy for Higher Educational Institutions
		3.3 State-of-the-Art Problem
			3.3.1 Role of Virtualization in Resource Provisioning and Allocation
	4 Definitions for SLA
		4.1 Components of SLA
		4.2 Phases in SLA Lifecycle
		4.3 The Need for SLA Approach for Cloud
		4.4 Challenges in SLA-Based Resource Provisioning Using Virtualization
			4.4.1 DPS-Yemane-Shareme CSMM Model for Client-Side SLA
	5 Case Study: The Emergence of Micro Service and Containerization
		5.1 What Is a Micro Service?
		5.2 The Containerization of Services
	6 Conclusion
	References
A Holistic Approach: Issues and Challenges in Autonomic Computation Toward Industry 4.0
	1 Introduction
	2 Industry IoT in Manufacturing
	3 Characteristics of Industry 4.0
		3.1 Vertical Networking for Smart Manufacturers
		3.2 Horizontal Integration of Technology Over Generations
		3.3 Smart Engineering for Business Value Chain
		3.4 Acceleration for Emerging Technologies
	4 Elements of Industry 4.0
	5 Autonomic Computing Challenges
	6 Autonomic Computing Industry 4.0 Solutions
		6.1 Solution for Vertical Networking of Smart Manufacturers
		6.2 Solutions for Horizontal Integration of Technology Over Generations
		6.3 Solutions for Smart Engineering of Business Value Chain
		6.4 Solutions for Acceleration for Emerging Technologies
	7 Future Trends in Autonomic Computing
	References
Resource Management Issues and Challenges in AutonomicComputing
	1 Introduction
	2 Autonomic Computing
		2.1 Evaluation
		2.2 Frameworks
		2.3 Applications
			2.3.1 Applications for Self-Healing Systems Autonomic Computing
			2.3.2 Requests of Autonomic Computing in Virtualized Environment
			2.3.3 Autonomic Computing Applications for Business
	3 Literature Review
	4 Issues and Challenges
		4.1 Autonomic System Challenges
			4.1.1 Relationships Among Autonomic Elements (AEs)
			4.1.2 Learning and Optimization Theory
			4.1.3 Robustness
		4.2 Issues in Open Autonomic Personal Computing
			4.2.1 Security
			4.2.2 Connectivity
			4.2.3 Storage
			4.2.4 Peer Group Collaboration
			4.2.5 Network-Based Services
			4.2.6 User Interface
		4.3 Challenges in Autonomic Benchmarking
			4.3.1 Injecting Changes
			4.3.2 Metrics and Scoring
			4.3.3 Handling Partially Autonomic Systems
		4.4 Autonomic Computing Research Problems
			4.4.1 Conceptual
			4.4.2 Architecture
			4.4.3 Middleware
			4.4.4 Application
		4.5 Technology Transfer Issues of Autonomic Computing
			4.5.1 Trust
			4.5.2 Economics
			4.5.3 Standards
		4.6 Open Problems in Autonomic Computing
			4.6.1 How to Select the Right Formalism and Techniques?
			4.6.2 How to Incorporate Autonomic Behavior to Non-Autonomous or Semi-Autonomous Systems?
			4.6.3 How to Study and Administer the Dependencies between Autonomous Elements to Address Business Policies?
			4.6.4 How to Make More Open/Extensible Autonomic Tools?
			4.6.5 What Are the Major and Minor Characteristics for the Evaluation of Autonomic Systems?
	5 Automatic Virtual Resource Management in Cloud
		5.1 System Architecture
		5.2 The LDM
		5.3 The GDM
			5.3.1 VM Provisioning
			5.3.2 VM Packaging
	6 Conclusion
	References
Classification of Various Scheduling Approaches for Resource Management System in Cloud Computing
	1 Introduction
		1.1 Re-Orientation Motivation
	2 Background
		2.1 Coming Up Next Is, for the Most Part, Referred to Implications of Cloud Computing
	3 Classification of Cloud Resources
	4 Literature Survey
	5 Classification of Resource Scheduling
	6 Conclusion
	References
Optimization in Autonomic Computing and Resource Management
	1 Introduction
		1.1 What Is the Need for Optimization in Resource Management?
	2 Related Work
	3 Optimization Algorithms
		3.1 Types of Optimization Algorithms
		3.2 Why to Study Meta-heuristic Algorithms?
		3.3 Types of Meta-Heuristic Algorithms
	4 Conclusion
	References
A Proposed Framework for Autonomic Resource Management in Cloud Computing Environment
	1 Introduction
	2 Cloud Computing
	3 Autonomic Computing in Cloud Computing
		3.1 Applications of Autonomic Computing
			3.1.1 Autonomic Computing for Business Applications
			3.1.2 Autonomic Computing for CRM
			3.1.3 Autonomic Computing for ERP
		3.2 Challenges of Autonomic Computing
	4 Related Work
	5 Generic Architectures
	6 Proposed Architectures
	7 Conclusion
	References
Autonomic Computing on Cloud Computing Using Architecture Adoption Models: An Empirical Review
	1 Introduction
	2 Literature Review
		2.1 Self-Management Attributes and Capabilities of Autonomic Computing
		2.2 Self-Sufficient Device Skills
		2.3 Autonomic Computing Architectures
	3 Adoption Models and Requirements of Autonomic Computing
		3.1 Plan for Cloud Protection Management
		3.2 Monitored Data Processing
		3.3 Political Response Method
		3.4 Autonomic Cloud Computing Properties
	4 Autonomous Computing Gains
	5 Performance Evaluation
	6 Conclusion
	References
Self-Protection Approach for Cloud Computing
	1 Introduction
	2 Literature Review
		2.1 Secure: Self-Protection Approach in Cloud Resource Management [5]
		2.2 Cloud Computing Security: A Survey [6]
		2.3 MeghRaj: A Cloud Environment for e-Governance in India [7]
		2.4 Cloud Computing: Different Approach and Security Challenge [8]
	3 Concept of Cloud Security
		3.1 Meaning
		3.2 Importance of Cloud Security
		3.3 Types of Cloud Computing Services
	4 Securities, Challenges, and Architecture
		4.1 Overview
		4.2 Security Challenges in Cloud Computing
		4.3 Degree of Security at Different Levels of Service
		4.4 Cloud Security Models
		4.5 How Security Changes with Cloud Networking
		4.6 CSA Stack Model
	5 Security Guidelines, Measures, and Technique
		5.1 Enabling Activities for GI Cloud
		5.2 Cloud Security
	6 Conclusions and Suggestions
	References
Elastic Security for Autonomic Computing Using Intelligent Algorithm
	1 Introduction
	2 Motivation for Elastic Security in Cloud Computing
	3 Challenges of Elastic Security in Cloud Computing
	4 Security and Privacy of Data Approaches
		4.1 Access Control Mechanism
		4.2 Identity and Access Management (IAM)
		4.3 Homomorphic Encryption
		4.4 Anonymity
		4.5 Securing Information Through Hardware Approach
		4.6 Threat Modeling
		4.7 Third-Party Auditor (TPA) for Privacy Preservation
		4.8 Auditing of Service-Level Agreement (SLA)
		4.9 Security and Privacy Issues in Virtualization
		4.10 Securing Data in Cloud Through Approach of Merging
	5 Role of Key Management for Providing Cloud Security
		5.1 User-Managed Key Management
		5.2 Public Key Encryption (PKE)
		5.3 Proxy Re-encryption
		5.4 Certificate-Less Encryption
		5.5 Convergent Key Management
		5.6 Group Key Management
		5.7 Attribute-Based Key Management
		5.8 Threshold Cryptography-Based Key Management
	6 Secret Sharing Algorithms
	7 Results and Analysis
	8 Security Analysis
	9 Machine Learning Algorithm to Identify Threat Patterns that Enable Security of Cloud
	10 Summary
	References
The Architecture of Autonomic Cloud Resource Management
	1 Introduction
		1.1 What is Autonomic Computing
		1.2 Why Autonomic Computing Is Used
		1.3 Role of Autonomic Cloud Resource Management in Autonomic Computing
	2 Literature Survey
		2.1 Investigation Method
		2.2 Problem Identification and Challenges in Autonomic Cloud Resource Management
	3 Methodology
	4 Conclusion and Future Research Scope
	Appendix
	References
Towards Industry 4.0 Through Cloud Resource Management
	1 Introduction
		1.1 Resource Management Is Essential
		1.2 Research Scope and Motivation
	2 Classification of Resource Management(RM) Policies
		2.1 Resource Management (RM) Mechanisms
	3 A General Model for Resource Management Mechanism
		3.1 Role of Essential Factors in Resource Management
	4 Taxonomy of Resource Management(RM) Policies
		4.1 Related Surveys
		4.2 RM Techniques Based on Load Balancing
		4.3 RM Techniques Based on Energy Optimization
		4.4 RM Techniques Based on Cost
		4.5 RM Techniques Based on SLA Violations
		4.6 RM Techniques Based on Resource-Aware Scheduling
		4.7 Autonomic Resource Management
	5 Performance Metrics to Evaluate RM Techniques
	6 Research Issues and Challenges in RM
	7 Conclusion
	References
A Walkthrough in Live Migration Strategies for Energy-Aware Resource Management in Cloud
	1 Introduction of Cloud Computing
		1.1 Challenges of Cloud Computing
	2 Overview of Resource Management
		2.1 Resource Allocation Strategies: An Elemental Walkthrough
		2.2 Existing Resource Scheduling Models in Cloud Computing
	3 Energy Management in Cloud
		3.1 Motivation of Research
		3.2 Existing Energy Management Scheduling Strategies
		3.3 Contribution of Live Migration in Energy Management Scheduling Strategies
	4 Discussion for Future Work
	5 Conclusion
	References
Virtual Machine Scaling in Autonomic Cloud Resource Management
	1 Introduction to Virtualization and Virtual Machine Scaling
	2 Need of Virtual Machine Scaling
	3 Methods to Implement Scaling
		3.1 Vertical Scaling
			3.1.1 Memory Scaling Algorithm
			3.1.2 CPU Scaling Algorithm
		3.2 Horizontal Scaling
	4 Modes or Policies
		4.1 Automatic Mode
			4.1.1 Reactive Mode
			4.1.2 Proactive Mode
			4.1.3 Time Series Analysis
			4.1.4 Model Solving Mechanisms
			4.1.5 Combination of Both Reactive and Proactive
			4.1.6 Control Theory
			4.1.7 Queuing Theory
			4.1.8 Reinforcement Learning (RL)
			4.1.9 Comparison Between Various Techniques of Reactive and Proactive Mode
		4.2 Programmable Mode
	5 Research Challenges
	6 Conclusion
	References
Autonomic Resource Management in a Cloud-Based Infrastructure Environment
	1 Introduction
	2 Literature Review
		2.1 E-commerce J2EE Applications
		2.2 Hosting Data Center Environment
	3 Cloud Computing Methodology
		3.1 Public Cloud versus Private Cloud
		3.2 Selection Between a Public and a Private Cloud: A Case Study
		3.3 Private Cloud
		3.4 Uses of Cloud Computing
		3.5 Data Center
		3.6 IaaS in Public Domain
		3.7 PaaS (Platform as a Service)
		3.8 SaaS (Software as a Service)
		3.9 Challenges in Cloud Computing
	4 Autonomic Computing
		4.1 Definition
		4.2 Technical View
		4.3 Self-Management Attributes of System Components
		4.4 Self-Configuration
		4.5 Self-Healing
		4.6 Self-Optimizing
		4.7 Self-Secure
	5 An Autonomic Engine for Managing Clouds
		5.1 Autonomic Engine Introduction
		5.2 Gathering Resource Metadata
		5.3 Requirements for Monitoring
	6 Experimental Analysis and Results
	7 Conclusion and Future Research Directions
	References
Digital Dimensions of Industry 4.0: Opportunities for Autonomic Computing and Applications
	1 Introduction
	2 Related Work
	3 First Industrial Revolution to Fourth Industrial Revolution: An Evolution/Synopsis
	4 First Industrial Revolution
	5 Second Industrial Revolution
	6 Third Industrial Revolution
	7 Fourth Industrial Revolution
	8 Fundamental Technology Drivers of Industry 4.0
		8.1 Infrastructure
			8.1.1 Internet of Things (IoT)
			8.1.2 Cyber-Physical System (CPS)
			8.1.3 Cloud Computing
		8.2 Software Applications/Tools
			8.2.1 Information and Communications Technology (ICT)
			8.2.2 Robotics Process Automation (RPA)
			8.2.3 Artificial Intelligence (AI)
			8.2.4 Graph Theory
			8.2.5 Digital Twin
		8.3 Process
			8.3.1 Big Data Analytics
	9 Reenvisioning Manufacturing Industry
		9.1 A Journey Toward Smart Manufacturing
			9.1.1 Data Democracy: Directing Data Economy at the Ecosystem of Manufacturing Industry
			9.1.2 Data Points for Analytics: Data Generating Functions and Units
		9.2 IoT-Enabled Manufacturing
			9.2.1 Implementation of IIoT
		9.3 Industrial Robotics and Automation
		9.4 Cloud Manufacturing
	10 Design Principles
	11 Research and Innovation Directions
	12 Conclusion
	References
A Study of Resource Management and Security-Based Techniques in Autonomic Cloud Computing
	1 Introduction
	2 Related Work
	3 Security-Aware Resource Management Techniques in ACC
	4 Conclusion and Future Work
	References
Index