Swarm Intelligence for Cloud Computing

✍ Scribed by Indrajit Pan (editor), Mohamed Abd Elaziz (editor), Siddhartha Bhattacharyya (editor)

• Publisher: CRC Press
• Year: 2020
• Tongue: English
• Leaves: 219
• Category: Library

✦ Synopsis

Swarm Intelligence in Cloud Computing is an invaluable treatise for researchers involved in delivering intelligent optimized solutions for reliable deployment, infrastructural stability, and security issues of cloud-based resources. Starting with a bird’s eye view on the prevalent state-of-the-art techniques, this book enriches the readers with the knowledge of evolving swarm intelligent optimized techniques for addressing different cloud computing issues including task scheduling, virtual machine allocation, load balancing and optimization, deadline handling, power-aware profiling, fault resilience, cost-effective design, and energy efficiency.

The book offers comprehensive coverage of the most essential topics, including:

• Role of swarm intelligence on cloud computing services
• Cloud resource sharing strategies
• Cloud service provider selection
• Dynamic task and resource scheduling
• Data center resource management.

Indrajit Pan

is an Associate Professor in Information Technology of RCC Institute of Information Technology, India. He received his PhD from Indian Institute of Engineering Science and Technology, Shibpur, India. With an academic experience of 14 years, he has published around 40 research publications in different international journals, edited books, and conference proceedings.

Mohamed Abd Elaziz

is a Lecturer in the Mathematical Department of Zagazig University, Egypt. He received his

PhD from the same university. He is the author of more than 100 articles. His research interests include machine learning, signal processing, image processing, cloud computing, and evolutionary algorithms.

Siddhartha Bhattacharyya

is a Professor in Computer Science and Engineering of Christ University, Bangalore. He received his PhD from Jadavpur University, India. He has published more than 230 research publications in international journals and conference proceedings in his 20 years of academic experience.

✦ Table of Contents

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