Swarm Intelligence for Cloud Computing

Cover
Half Title
Title Page
Copyright Page
Dedication
Table of Contents
Preface
Editors
Contributors
1 A Survey of Swarm Intelligence for Task Scheduling
	1.1 Introduction
	1.2 Task Scheduling Problem Formulation
	1.3 The Swarm Intelligence Application for Cloud Computing
		1.3.1 Particle Swarm Optimization
		1.3.2 Artificial Bee Colony Algorithm
		1.3.3 Lion Optimization Algorithm
		1.3.4 Whale Optimization Algorithm
		1.3.5 Gray Wolf Optimization
		1.3.6 Bat Algorithm
		1.3.7 Cat Swarm Optimization
		1.3.8 Cuckoo Search Algorithm
		1.3.9 Hybrid Swarm Algorithm
		1.3.10 Multi-Objective Swarm Optimization
	1.4 Conclusion
	Bibliography
2 Techniques for Resource Sharing in Cloud
	2.1 Introduction
	2.2 Intercloud
		2.2.1 Resource Sharing in Federated Cloud
			2.2.1.1 Agent-Based (Centralized) Resource Sharing in Federated Cloud
			2.2.1.2 Peer-to-Peer Resource Sharing in Federated Cloud
		2.2.2 Resource Sharing in Multi-Cloud
			2.2.2.1 Agent-Based Service Selection Approaches in Multi-Cloud
			2.2.2.2 QoS-Based Service Selection in Multi-Cloud
	2.3 Social Cloud
	2.4 Mobile Cloud
		2.4.1 Incentive Mechanisms in Mobile Cloud
	2.5 Cloud Manufacturing
		2.5.1 Resource Sharing in CM
	2.6 Vehicular Cloud
		2.6.1 Vehicular Cloud Architecture
			2.6.1.1 Inside-Vehicle Layer
			2.6.1.2 Communication Layer
			2.6.1.3 Cloud Layer
		2.6.2 Resource Sharing in Trac Management
		2.6.3 Resource Sharing in Multimedia Services
		2.6.4 Resource Sharing in Smart Parking
		2.6.5 Vehicle-Assisted Cloud Computing (VACC) for Smartphones
		2.6.6 Resource Sharing in Vehicular Fog Computing
	2.7 Green Cloud
		2.7.1 Resource Sharing in GCC
	2.8 Resource Scheduling Using Meta-Heuristic Techniques
	Future Scope
	Summary
	Bibliography
3 Swarm Intelligent Techniques for Cloud Service
	3.1 Introduction
	3.2 Related Work
	3.3 Problem Description
		3.3.1 Context
		3.3.2 Model Formulation
		3.3.3 Decision Variable
		3.3.4 Objective Functions
	3.4 Solution Algorithms
		3.4.1 Solution Evaluation
		3.4.2 Genetic Algorithm (GA)
		3.4.3 Particle Swarm Optimization (PSO)
		3.4.4 Harmony Search (HS)
	3.5 Numerical Experiments
	3.6 Algorithms' Performance
	3.7 Conclusion and Future Work
	Appendix A: Example of Case Study Data
	Bibliography
4 Reliable Data Auditing and ACO-Based
	4.1 Introduction
	4.2 Basic Concepts
		4.2.1 Basic Outline of Ant Colony Optimization Method
		4.2.2 Concept of Merkle Tree
	4.3 Problem Definition
	4.4 Proposed Solution
		4.4.1 Dynamic Resource Scheduling
		4.4.2 Ant Colony Optimization-Based Resource Scheduling
			4.4.2.1 Pheromone Function
			4.4.2.2 Decision on Resource Scheduling
		4.4.3 Auditing of User Data
	4.5 Results and Discussion
		4.5.1 Metadata Generation Time for Data Files of Varying Sizes
		4.5.2 Data Verification with Varying Numbers of Tenants
		4.5.3 Successful Resource Allocation with Dierent Volumes of Active Users
		4.5.4 Balanced Allocation of Server Resource
	4.6 Conclusion
	Bibliography
5 TS-GWO: IoT Tasks Scheduling in Cloud
	5.1 Introduction
	5.2 Related Works
		5.2.1 Challenges
	5.3 System Model
		5.3.1 Multi-objective Design Model
		5.3.2 Solution Encoding
		5.3.3 Gray Wolf Optimizer for Solving the TS
			5.3.3.1 Inspiration Source
			5.3.3.2 Mathematical Model and Algorithm
	5.4 Experimental Results and Discussions
		5.4.1 Evaluation Measurements
		5.4.2 Results and Discussions
	5.5 Conclusion and Future Work
	Bibliography
6 Fact-Checking: Application-Awareness in
	6.1 Introduction
	6.2 Application-Awareness in Cloud Data Centers
	6.3 Background
	6.4 Research Directions
		6.4.1 Application-Aware Data Center Resource Management
		6.4.2 Topology of Inference Schemes
			6.4.2.1 Managing Cloud Services Admission
			6.4.2.2 Intelligent Resource Administration and Resource Optimization
	6.5 Conclusion
	Bibliography
7 Bio-inspired Optimization Algorithms for Multi-objective
	7.1 Introduction
	7.2 Related Work
		7.2.1 Contribution of Chapter
	7.3 Mathematical Formulation
		7.3.1 Multi-objective Optimization
	7.4 Solution Methodologies
		7.4.1 Particle Swarm Optimization Technique
		7.4.2 Artificial Bee Colony Algorithm
		7.4.3 Genetic Algorithm
		7.4.4 Ant Colony Optimization Algorithm
	7.5 Experimental Settings
	7.6 Results and Discussion
	7.7 Conclusion
	Bibliography
Index