This book explores the most recent Edge and Distributed Cloud computing research and industrial advances, settling the basis for Advanced Swarm Computing developments. It features the Swarm computing concepts and realizes it as an Ad-hoc Edge Cloud architecture.
Unlike current techniques in Edge and Cloud computing that solely view IoT connected devices as sources of data, Swarm computing aims at using the compute capabilities of IoT connected devices in coordination with current Edge and Cloud computing innovations. In addition to being more widely available, IoT-connected devices are also quickly becoming more sophisticated in terms of their ability to carry considerable compute and storage resources. Swarm computing and Ad-hoc Edge Cloud take full advantage of this trend to create on-demand, autonomic and decentralized self-managed computing infrastructures.
Focusing on cognitive resource and service management, the book examines the specific research challenges of the Swarm computing approach, related to the characteristics of IoT connected devices that form the infrastructure. It also offers academics and practitioners insights for future research in the fields of Edge and Swarm computing.
Author(s): Ana Juan Ferrer
Publisher: Springer
Year: 2023
Language: English
Pages: 196
City: Cham
Disclaimer
Contents
About the Author
1 Introduction
1.1 Motivation
1.2 Swarm Computing and Ah-hoc Edge Clouds
1.3 Book Organisation
References
Part I Current Status of Computing at the Cloud and Network Edges
References
References
2 Setting the Scene: Cloud, Edge, Mobile and Ad-hoc Computing Context
2.1 Cloud, Edge, Mobile and Ad-hoc Computing Relations
2.2 Decentralisation of Cloud Computing
2.3 Edge and Fog Computing Terminology
References
3 Cloud Computing
3.1 Introduction to Cloud Computing
3.2 Basic Definitions, Benefits and Drawbacks
3.3 Cloud Foundations
3.3.1 Virtualisation and Containerisation Technologies
3.3.2 Cloud Native Software Architectures
3.4 Services in Public Clouds
3.4.1 Infrastructure-as-a-Service (IaaS)
3.4.2 Platform-as-a-Service (PaaS)
3.4.2.1 Programming Frameworks and Tools
3.4.2.2 Data Management Services
3.4.2.3 Specialised Programming Approaches
3.5 Hybrid and Multi-Cloud Management
References
4 Mobile Cloud Computing
4.1 Introduction to Mobile Cloud Computing
4.2 MCC Challenges
4.2.1 Inherent Mobile Devices Challenges
4.2.2 Network Connectivity
4.2.3 Security
4.2.4 Off-Loading and Application Partitioning
4.3 MCC Models
4.4 Analysis of Existing Works in MCC
4.4.1 Approaches Based on Off-Loading to a Server
4.4.1.1 MAUI, Making Smartphones Last Longer with Code Offload
4.4.1.2 Cuckoo, a Computation Offloading Framework for Smartphones
4.4.2 Approaches Based on Off-Loading to Public/Private Cloud Computing
4.4.2.1 CloneCloud, Elastic Execution Between Mobile Device and Cloud
4.4.2.2 ThinkAir
4.4.3 Approaches Based on Off-Loading to Cloudlets
4.4.3.1 The Case for VM-Based Cloudlets in Mobile Computing
4.4.3.2 Gabriel
4.4.4 Approaches Based on Off-Loading to Other Mobile Devices
4.4.4.1 Hyrax, Cloud Computing on Mobile Devices Using Map Reduce
4.4.4.2 A Virtual Cloud Computing Provider for Mobile Devices
4.4.5 Features Comparison
References
5 Mobile Ad-hoc Cloud Computing
5.1 Introduction to Mobile Ad-hoc Cloud Computing (MAC)
5.2 MAC Challenges
5.3 MAC Models
5.4 Analysis of Existing Works in MAC
5.4.1 Dynamic Mobile Cloud Computing: Ad Hoc and Opportunistic Job Sharing
5.4.2 MoCCA, A Mobile Cellular Cloud Architecture
5.4.3 Ad-hoc Cloud as a Service
5.4.4 MobiCloud
5.4.5 mClouds
5.4.6 Aura
5.5 Features Comparison
References
6 Edge and Fog Computing
6.1 Computing Perspective, Edge and Cloud
6.1.1 Edge Computing Challenges
6.1.1.1 Edge Management
6.1.1.2 Edge Interoperability
6.1.1.3 Cognitive Techniques: AI and ML Applied to Edge Management
6.1.1.4 Economy
6.1.1.5 Eco-Efficiency
6.1.1.6 Security and Privacy
6.1.1.7 Connectivity and Resilience
6.1.2 Edge Computing Models
6.1.3 Existing Works in Research
6.1.3.1 Fog Computing, a Platform for Internet of Things and Analytics
6.1.3.2 ANGELS for Distributed Analytics in IoT
6.1.3.3 Mobile Fog
6.1.3.4 Nebula
6.1.3.5 Resource Provisioning for IoT Services in the Fog
6.1.4 Existing Products in the Market
6.1.4.1 Azure IoT Edge
6.1.4.2 AWS Greengrass
6.1.5 Existing Open-Source Initiatives
6.1.5.1 K3s
6.1.5.2 Microk8s
6.1.5.3 KubeEdge
6.1.5.4 Starlingx
6.1.5.5 ONEDge
6.1.5.6 IoFog
6.1.5.7 EdgeX Foundry
6.1.6 Features Comparison
6.2 Mobile Edge Computing and Networking Perspectives
6.2.1 ETSI Multi-Access Edge Computing Framework and Reference Architecture
6.2.2 Existing Products in the Market
6.2.2.1 Amazon Web Services 5G telco Offerings
6.2.2.2 Azure 5G telco Offerings
6.2.2.3 Google Cloud 5G telco Offerings
6.2.3 Conclusions
References
7 Additional Technologies for Swarm Development
7.1 Security Requirements for Computing at the Edge
7.2 The Role for P2P and Consensus Algorithms
References
Part II Computing Beyond the Edge: Swarm Computing and Ad-hoc Edge Architectures
References
References
8 Computing Beyond Edge: The Swarm Computing Concept
8.1 Overview
8.2 Foreseen Evolution Towards Swarm Computing
8.3 Definition of Ad-hoc Edge Clouds, the Swarm Computing Concept
8.4 Swarm Computing Characteristics and Principles
8.4.1 Swarm Characteristics
8.4.2 Key Principles
8.4.2.1 Aware
8.4.2.2 Autonomous
8.4.2.3 Actionable
8.5 Ad-hoc Edge Cloud Resources Characteristics
8.6 Lifecycle of a Swarm
8.7 Swarm Computing Motivational Use Cases
References
9 Building Blocks for Ad-hoc Edge Clouds
9.1 Introduction
9.2 Ad-hoc Edge Cloud Framework
9.2.1 Edge Device Context
9.2.2 Ad-hoc Edge Context
9.2.3 Ad-hoc Edge Cloud Architecture Flow of Events
9.2.4 Conclusions
References
10 Cognitive Resource Management in Ad-hoc Edge Clouds
10.1 Overview
10.2 IoT Device Availability Protocol
10.2.1 Publication
10.2.2 Registration
10.2.3 Select
10.2.4 Use
10.2.5 Release
10.2.6 Un-register
10.3 Ad-hoc Edge Cluster Instantiation and Management
10.3.1 Ad-hoc Edge Cluster Instantiation
10.3.2 Ad-hoc Edge Cluster Management
10.3.2.1 Cluster Operation
10.3.2.2 Node Addition
10.3.2.3 Node Failure
10.4 Evaluation
10.4.1 Lab Evaluation
10.4.1.1 Scalability Experimentation
10.4.1.2 Availability/Churn Rates Experimentation
10.4.2 Large Scale Evaluation in AWS EC2
10.4.2.1 Scalability Experimentation
10.4.2.2 Large Scale Evaluation via AWS A1
10.5 Conclusions
References
11 Service Placement and Management
11.1 Overview
11.1.1 Admission Control in Service Lifecycle of Ad-hoc Edge Infrastructure
11.2 Ad-hoc Edge Service Model
11.3 Admission Control Mechanism Formulation
11.3.1 Resource Availability Prediction
11.4 Evaluation
11.4.1 Node Quality
11.4.2 Service Quality
11.5 Related Works
11.6 Conclusions
References
Part III Looking Ahead, Next Steps for Ad-hoc Edge Clouds and Swarm Computing Realization
12 Next Steps for Ad-hoc Edge Cloud and Swarm Computing Realization
12.1 Edge Computing Research Areas
12.1.1 Heterogeneity Exploitation and New Hardware Architectures: Neuromorphic Edge Computing
12.1.2 Energy Efficiency Optimisation
12.1.3 Multi-Level Edge
12.1.4 Edge Intelligence
12.1.5 Data Management
12.1.6 Edge Management
12.1.7 Computing Continuum Exploration
12.2 Swarm Computing Research Areas
12.2.1 Swarm Management Techniques
12.2.2 Resource Discovery
12.2.3 Self-management and Autonomic Systems
12.2.4 Bio Inspired Optimisation Techniques
References
