Preface
About This Book
Contents
About the Editors
Part I: Multi-cloud Environments for Enterprise-Grade Applications
Chapter 1: Invocation of Multi-Cloud Infrastructure Services in Web-Based Semantic Discovery System
1.1 Introduction
1.2 Literature Review
1.3 Proposed Methodology
1.4 Implementation
1.4.1 Building the Cloud Ontology
1.5 Experimental Results and Discussion
1.6 Conclusion
References
Chapter 2: Hybrid Machine Learning Models for Distributed Biological Data in Multi-Cloud Environment
2.1 Introduction
2.1.1 Chapter Sections Overview
2.2 Literature Review
2.3 Hybrid Models of Deep Learning and Machine Learning
2.4 Experimental Results and Discussion
2.4.1 Accuracy
2.4.2 Precision/Specificity
2.4.3 Recall/Sensitivity
2.4.4 F-Measure
2.4.5 Time
2.4.6 Motif for Normal Sequence (Fig. 2.8)
2.4.7 Motif for Mutant Sequence (Fig. 2.9)
2.5 Conclusion
References
Chapter 3: Multi-Cloud Path Planning of Unmanned Aerial Vehicles with Multi-Criteria Decision Making: A Literature Review
3.1 Introduction
3.2 Classification of UAV
3.2.1 Based on Aerodynamics
3.2.2 Based on Landing
3.2.3 Based on Weight and Range
3.3 Hardware Design and Challenges
3.4 Path Planning Overview and Issues
3.4.1 Steps in the Path Planning
3.4.2 Challenges in Path Planning
3.4.2.1 Multi-Cloud Path Planning with Multi-criteria Decision Making
3.4.2.2 Internet of Drone-Based Data Analytics in Multi-Cloud
3.4.3 Path Planning Techniques in UAVs
3.4.3.1 Representation Techniques
Sampling-Based Techniques
Cell Decomposition
Roadmaps
Artificial Intelligence Techniques
Heuristic-Search Techniques
Brute-Force Search Techniques
Local Search Techniques
Artificial Neural Networks
3.4.3.2 Cooperative Techniques
Machine Learning Models
Multi-objective Optimization Models
Mathematical Models
Bio-Inspired Models
3.4.3.3 Non-cooperative Techniques
Coverage and Connectivity
3.4.3.4 Multi-Cloud Security in UAV Path Planning
3.5 Conclusion
References
Chapter 4: Estimation of Sharing Dependencies in Personal Storage Clouds Using Ensemble Learning Approaches
4.1 Dynamic Provisioning of Cloud Resources in Multi-Cloud Environment
4.1.1 Storage Cloud
4.1.2 Dynamic Provisioning of Personal Storage Cloud
4.2 Machine Learning in Cloud Resource Provisioning
4.3 Prediction of Sharing Dependency Using Ensemble Learning
4.3.1 Ensemble Learning
4.3.2 Dataset
4.3.3 Preprocessing
4.3.4 Ensemble Classifiers
4.3.5 Data Cleaning
4.3.6 Model Training and Analysis
4.3.7 Comparative Results
4.4 Conclusions
References
Part II: Multi-cloud Setup and Resource Management
Chapter 5: Resource Management Framework Using Deep Neural Networks in Multi-Cloud ÂEnvironment
5.1 Introduction
5.2 Problem Formulation
5.3 Proposed Method
5.3.1 Scheduling Using GWO
5.3.2 Load Balancing
5.4 Results and Discussions
5.4.1 Simulation Results and Discussions
5.5 Conclusions
References
Chapter 6: SLA-Based Group Tasks Max-Min (GTMax-Min) Algorithm for Task Scheduling in Multi-Cloud Environments
6.1 Introduction
6.2 Related Works
6.3 Problem Definition
6.3.1 Environment Setup
6.3.2 Model for Application and Problem Definition
6.3.2.1 Expected Time to Compute Matrix (ETC)
6.3.2.2 Expected Gain (EGM) Matrix
6.3.2.3 Expected Penalty (EPM) Matrix
6.3.2.4 Agreement Level of Service
6.3.2.5 SLA-GTMax-Min Scheduling Algorithm
6.3.2.6 An Illustration
6.4 Evaluation Parameters
6.4.1 Makespan
6.4.2 Average Cloud Utilization
6.4.3 Gain Cost
6.4.4 Penalty Cost
6.5 Simulation Results
6.5.1 Benchmark Descriptions
6.6 Conclusion
References
Chapter 7: Workload Balancing in a Multi-Cloud Environment: Challenges and Research Directions
7.1 Introduction
7.1.1 Need for Multi-Cloud
7.1.2 Architecture of Multi-Cloud and Working Functionality of Multi-Cloud
7.1.2.1 Benefits of Multi-Cloud
7.2 Workload Balancing Among Multiple Clouds and Effect on Performance Parameters in a Multi-Cloud Environment
7.3 Current Challenges and Research Direction Toward the Multi-Cloud
7.3.1 Challenges in Multi-Cloud Architecture
7.4 Simulators to Solve Research Challenges in Multi-Cloud Management
7.4.1 Multi-Cloud Management Platforms Tools
7.4.2 Simulation Tool
7.4.2.1 CloudSim
7.4.2.2 CloudReports
7.4.2.3 Cloud Analyst
7.4.2.4 CloudSME
7.4.2.5 MDCSim
7.4.2.6 DCSim
7.4.2.7 SimIC
7.5 Conclusion
References
Chapter 8: Mediator-Based Effective Resource Allotment on Multi-Clouds
8.1 Introduction
8.2 Related Research Works
8.2.1 Overview of Multi-mediator Framework
8.2.2 Cloud Computing Resource Allocation Process
8.3 Mediator-Based Effective Resource Allotment
8.3.1 Problem Modeling Purchaser Mediator Has Accompanying Features
8.3.2 Negotiation Protocol
8.4 Resource Allotment Design Based on Multi-mediator Framework in Cloud Computing
8.4.1 Architecture Design
8.4.1.1 Cloud Resource Level
8.4.1.2 Cloud Resource Management Level
8.4.1.3 Cloud User Level
8.4.2 Organization Design of Multi-mediator Framework
8.4.3 Working Process of Multi-mediator Framework
8.4.4 Collaborative Process of Multi-mediator Framework
8.5 Simulation Experiment
8.6 Conclusions
References
Chapter 9: A Robust Communication Strategy for Inter-Cloud Networking Environment through Augmented Network-Aware and Multiparameter Assistance
9.1 Introduction
9.2 Related Works
9.3 Proposed System
9.3.1 Differentiation of Service
9.3.2 Congestion Detection
9.3.3 Inter-Node Data Transfer Rate or Injection Rate
9.3.4 Link Quality Estimation
9.3.5 Consolidated Grade Vector Estimation and Node Rank Index
9.4 Results and Discussion
9.5 Conclusion
References
Part III: Next Generation Technologies for Multi-cloud
Chapter 10: An Intense Study on Intelligent Service Provisioning for Multi-Cloud Based on Machine Learning Techniques
10.1 Introduction
10.1.1 Cloud Computing and Its Services
10.1.2 Multi-Cloud Computing: Need, Terminologies, and Challenges
10.2 Classification of Services Provisioning in Multi-Cloud
10.2.1 Objectives, Topologies, Requirements, and Procedures in Service Provisioning of Multi-Cloud
10.2.1.1 Objectives in Service Provisioning of Multi-Cloud
Self-Service Provisioning and Autonomous Service Provisioning
Autonomous Workload Distribution
Elasticity
Removal of Latency Constraint: Location (Placement and Consolidation)
10.2.1.2 Topologies in Service Provisioning of Multi-Cloud
Service Provisioning Model
Brokerage-Aided Provisioning
Policy Ensure Service Level Agreements (SLAs)
Heuristic and Holistic Perspective
Multi-criteria Decision-Making (MCDM)
Algorithmic Techniques
Requirements in Service Provisioning of Multi-Cloud
Patterns Related to Distributed Deployment of Applications
Patterns Related to Redundant Deployment of Applications
10.2.1.3 Procedures in Service Provisioning of Multi-Cloud
User Request Scheduling
Service Selection
Service Composition
Service Monitoring and Orchestration
10.3 Intelligent Service Provisioning (ISP) in Multi-Cloud
10.3.1 ISP: Methodologies, Advantages, and Limitations in Multi-Cloud Environment
10.3.1.1 ISP Based on Characteristics of Service Provisioning
Intelligent Service Provisioning for Workload Management
Intelligent Service Provisioning for Providing Elasticity
Intelligent Service Provisioning for Removing Latency Constraint (Placement and Consolidation)
10.3.1.2 ISP Based on Approaches of Service Provisioning
ISP Based on Service Provisioning Models
ISP Based on Brokerage-Aided Provisioning
ISP Based on Policy Ensure Service Level Agreements (SLAs)
ISP Based on Heuristic and Holistic Perspective
ISP Based on Multi-criteria Decision-Making (MCDM)
ISP Based on Algorithmic Techniques
10.3.1.3 ISP Based on Procedures of Service Provisioning
ISP Based on User Request Scheduling
ISP Based on Service Selection Procedure
ISP Based on Service Composition
ISP Based on Service Monitoring and Orchestration
10.3.2 Comparison of Various Intelligent Middleware for Management of Multi-Cloud Services
10.4 Benchmark Case Studies: ISP Provisioning in Multi-Cloud
10.4.1 Case Study 1: Multi-Cloud Framework with ISP in Healthcare
10.4.2 Case Study 2: Multi-Cloud Framework with ISP in Industrial IOT and Smart City Services
10.5 Review on State-of-the-Art ML Algorithms for Service Provisioning in Multi-Cloud Environment
10.5.1 Neural Network (NN)
10.5.2 Reinforcement Learning
10.5.3 Support Vector Machine (SVM)
10.5.4 Deep Belief Network (DBN)
10.5.5 Principal Factor Analysis (PFA)
10.5.6 Random Forest, AdaBoost and XgBoost
10.5.7 Hierarchical Clustering
10.6 Framework for Intelligent Service Provisioning in Multi-Cloud
10.7 Challenges and Future Prospects
10.7.1 Challenges of Intelligent Service Provisioning (ISP) in Multi-Cloud Environment
10.7.2 Future Prospects of Intelligent Service Provisioning (ISP) in Multi-Cloud Environment
10.8 Conclusion
References
Chapter 11: Fuzzy-Based Workflow Scheduling in Multi-Cloud Environment
11.1 Introduction
11.2 System Architecture
11.3 Multi-objective Fuzzy Decision Model
11.3.1 Membership Function for Trust Evaluation
11.3.2 Membership Function for Execution Time
11.3.3 Membership Function for Execution Cost
11.3.3.1 Fuzzy Decision
11.4 Schedule Primitives
11.5 Fuzzy Logic-Based Workflow Scheduling
11.6 Results and Discussions
11.7 Conclusion
References
Chapter 12: Performance Evaluation and Comparison of Hypervisors in a Multi-Cloud Environment
12.1 Introduction
12.2 Related Works
12.3 Methodology
12.3.1 Setting Up of the Virtual Machines
12.3.2 Setting Up of the Benchmark Tests
12.3.2.1 CPU Test
12.3.2.2 Memory Utilization Test
12.3.2.3 Disk Utilization Test
12.4 Implementation
12.4.1 Running Kali Linux on Windows Subsystem on Linux (HypervisorâI)
12.4.2 Running Kali Linux on VMware Workstation (Hypervisor-II)
12.4.3 Running Kali Linux on Oracle Virtual Box (Hypervisor-III)
12.5 Results and Discussion
12.6 Conclusion
References
Chapter 13: A Manifesto for Modern Fog and Edge Computing: Vision, New Paradigms, Opportunities, and Future Directions
13.1 Introduction
13.1.1 Motivation
13.1.2 Contributions
13.1.3 Chapter Structure
13.2 Background: Fog and Edge Computing
13.3 IoT Applications
13.3.1 Healthcare
13.3.2 Agriculture
13.3.3 Smart Home
13.3.4 Traffic Management
13.3.5 Weather Forecasting
13.4 Type of Architectures
13.5 Relevant Issues and Paradigms: A Vision
13.5.1 Service Level Agreement (SLA) and Quality of Service (QoS)
13.5.2 Energy Efficiency and Sustainability
13.5.3 Provisioning and Scheduling of Resources
13.5.4 Fault Tolerance (Reliability)
13.5.5 Interlayer Communication
13.5.6 Security and Privacy
13.5.7 Big Data Analytics
13.5.8 Internet of Things (IoT)
13.5.9 Data Processing
13.5.10 Application Design
13.5.11 Serverless Computing
13.5.12 Blockchain
13.5.13 Software-Defined Network (SDN)
13.5.14 Deep Learning (DL) and Artificial Intelligence (AI)
13.5.15 Containers
13.5.16 Quantum Computing
13.5.17 Cloud Mining (Bitcoin)
13.5.18 Serverless Edge Computing
13.5.19 6G Technology
13.5.20 Industry 4.0
13.5.21 Autoscaling
13.6 Opportunities and Future Directions
13.7 Summary and Conclusions
References
Part IV: Privacy and Security Issues in Multi-cloud Environment
Chapter 14: Functionalities and Approaches of Multi-cloud Environment
14.1 Introduction to Cloud Computing
14.1.1 Architecture of Cloud
14.1.2 Service Models
14.1.2.1 Cloud Infrastructure as a Service (IaaS)
14.1.2.2 Cloud Platform as a Service (PaaS)
14.1.2.3 Cloud Software as a Service (SaaS)
14.1.3 Deployment Models
14.2 Issues in Cloud Computing
14.3 Introduction to Multi-cloud
14.3.1 Single Cloud to Multi-cloud
14.3.2 Architecture of Multi-cloud
14.3.3 Benefits of Multi-cloud
14.4 Security in Multi-cloud Architecture
14.4.1 Algorithm for Data Security in Cloud
14.4.1.1 Encryption Algorithm
14.4.1.2 Data De-duplication Technology
14.4.1.3 Cloud Storage Technology
14.4.2 Good Practices in Multi-cloud Strategy
14.5 Conclusion
References
Chapter 15: Quality, Security Issues, and Challenges in Multi-cloud Environment: A Comprehensive Review
15.1 Introduction
15.2 Related Work
15.2.1 Cloud Computing and Cloud Systems
15.2.1.1 Safety Challenge in Cloud Computing
15.2.1.2 Multi-clouds and Multi-cloud Applications
15.2.2 Security Challenges in Multi-cloud Computing
15.2.3 Cloud Methods
15.2.4 Distributed Computing and XACML
15.2.5 Distributed Storage Intimidation and Attack Surfaces
15.2.6 Cloud Services Brokerage
15.2.6.1 Insightful Cloud Broker
15.2.6.2 Customer Portal
15.3 Security Issues
15.3.1 Interoperability Issues
15.3.2 Security or Confidentiality
15.3.3 Trustworthiness
15.3.4 Administration Availability or High Availability
15.3.5 Confirmation and Authorization
15.3.6 Information Proprietor
15.3.6.1 Mystery Key
15.3.7 Cloud Intermediary Design
15.4 Security Evaluation Framework
15.4.1 Structure Operations
15.4.2 Risk Identification and Risk Analysis
15.4.3 Choice of Metric and Safety Controls
15.4.4 Application Security Monitoring
15.4.5 Security Measurement
15.4.6 Dynamic and Safety Measures Status Perception
15.4.7 Data Possession in Multi-cloud Storage
15.5 Construction Mechanism
15.5.1 Request Engine
15.5.2 Security System
15.5.3 Monitoring System
15.5.4 Safety Measurement Engine (SME)
15.5.5 Decision System
15.6 Cost of the Administration in a Multi-cloud Environment
15.6.1 Costs Stood to Secure Cloud Administrations
15.6.2 Multi-exhausting in Cloud
15.6.2.1 Parcel of Use System into Levels
15.6.2.2 Segment of Use Information into Fragments
15.6.3 Service-Oriented Broker
15.6.3.1 Issues Identified with Repair Relocation on Cloud
15.6.3.2 Issues Identified with Privacy
15.7 Explicit Risk and Cost in Multi-cloud Environment
15.7.1 Multi-cloud Climate
15.8 Discussion
15.9 Conclusion
References
Chapter 16: Trust Management Framework for Handling Security Issues in Multi-cloud Environment
16.1 Introduction to Access Control Methods
16.1.1 Basic Access Control Models
16.1.2 Trust-Based Access Control Models
16.2 SLA and Trust Management
16.2.1 SLA in a Multi-Cloud Environment
16.2.2 Deployment of SLA in Multi-cloud Models
16.2.2.1 SLA in the Coalesced Cloud
16.2.2.2 SLA in the Distributed Cloud
16.2.2.3 SLA in the Hybrid Cloud
16.2.2.4 SLA in Multi-cloud Management Platform (M-CMP)
16.2.2.5 SLA in Cloud Service Brokerage (CSB)
16.2.3 Trust Management Methodology
16.2.4 Security SLA in Multi-cloud
16.3 Security Framework for Multi-cloud
16.3.1 Shift from Single Cloud to Multi-cloud
16.3.1.1 Single Cloud: Security Limitations
Integrity of Data
Confidentiality and Privacy of Data
Data Availability
16.3.1.2 Single Cloud Homomorphic Encryption
16.3.1.3 Security Mechanism in Multi-cloud
16.3.2 Cyber Security Framework
16.3.2.1 Cyber Security Challenges: Multi-cloud Environment
16.3.2.2 Ways to Handle Challenges in Multi-cloud Environment
16.3.3 Security Controls in Multi-cloud
16.4 Research Challenges
16.4.1 Authentication
16.4.2 Compliance
16.4.3 Vulnerability Management
16.5 Conclusion
References
Part V: Intelligent Broker Design for Multi-cloud Environment
Chapter 17: Intelligent Workflow Adaptation in Cognitive Enterprise: Design and Techniques
17.1 Evolution of Cognitive Computing and Cognitive Enterprise
17.1.1 Introduction
17.1.2 Cognitive Transformation
17.1.3 Cognitive Skills Necessary for Cognitive Enterprise Applications
17.1.4 Value Additions for Stakeholders from Cognitive Enterprise
17.2 Workflow Automation and Current Approaches
17.2.1 Workflows an Overview
17.2.2 Current Context of Business Workflows
17.2.3 Tools and Processes for Automated Workflow Management System
17.2.4 Workflows and Multi-cloud Environment
17.2.4.1 Multi-cloud: An Overview
17.3 Intelligent Workflow Approaches for Cognitive Enterprise
17.3.1 Intelligent Workflows with Intelligent Agents
17.3.2 Designing of Cognitive Workflows
17.3.3 Design Strategies for Intelligent Workflow Adaptation for Multi-cloud
17.3.4 Reengineering of Workflows
17.4 Issues and Challenges in Employing Intelligent Workflows in Multi-cloud
17.4.1 Challenges in Adapting Intelligent Workflows in Multi-cloud
17.4.2 Issues and Challenges in Multi-cloud Business Operations
17.4.3 Benefits of Multi-cloud for Business
17.4.4 Use of Intelligent Workflows in Multi-cloud
17.5 Conclusion
References
Chapter 18: Broker-Based Collaborative Auction Method for Resource Scheduling in Cloud Computing
18.1 Introduction
18.2 Related Works
18.3 System Design
18.3.1 System Model
18.3.2 Broker-Related Form
18.4 Broker-Based Collaborative Auction Method (BCA)
18.4.1 Fundamental Coordinating Stage
18.4.2 Forward Declaration Auction Stage
18.4.3 Reverse Declaration Auction Stage
18.4.4 Auction Computation
18.4.4.1 Forward Auction Calculation
18.4.4.2 Backward Auction Calculation
18.5 Broker-Based Collaborative Auction Scheduling Method (BCAS)
18.5.1 Responsibility About Manager Broker
18.5.2 Responsibility of Job Broker
18.5.3 Responsibility of VM Broker
18.6 Simulation Results
18.7 Conclusion and Future Work
References
Chapter 19: An Effective Cloud Broker Framework for Knowledge Discovery in Multi-Cloud Environment
19.1 Introduction
19.2 Related Work
19.3 System Architecture
19.3.1 Document Retrieval
19.3.2 Document Preprocessing
19.3.2.1 Lexical Analysis
19.3.2.2 Stop Words Removal
19.3.2.3 Stemming
19.3.2.4 Nouns Identification
19.3.2.5 Identifying Generic Form of Nouns
19.3.3 Document Classification
19.3.3.1 ODP Category Extraction
19.3.3.2 Categorization
Ant Colony Algorithms
Ant Miner Algorithm
Pheromone Initialization
Heuristic Function
Rule Construction
Rule Pruning
Pheromone Updation
19.3.3.3 User Model Construction
Building of User Interest Profile
Current Session Tracking
Building of User Model
19.3.3.4 Information Retrieval
Query Reformulation
Identification of Query Concept
Identification of User Interest
Query Transformation
Searching
Re-ranking
19.4 Results and Evaluation
19.5 Conclusion
References
Chapter 20: Effective Deployment of Multi-cloud Customizable Chatbot Application for COVID-19 Datasets
20.1 Introduction
20.2 Related Works
20.3 Cloud Framework for Chatbot Design Tools and Use Cases
20.4 Serverless Operational and Deployment Model
20.4.1 Different Forms of Responses in Taking Actions for the Questions
20.5 Sample Set of Intents
20.5.1 Categorization of Intents
20.6 Chatbot with Webhooks
20.7 Multi-cloud Environment for Deploying the Chatbot
20.8 Experimentation
20.9 Conclusion
References
Index