Hierarchical and Autonomous Fog Architecture

Cloud computing paradigm stops short in its offerings towards deployment of latency-critical and bandwidth-intensive applications. Fog computing paradigm emerged as a promising solution to realize deployment of large scale IoT environments and low latency real-time services, leveraging large number of resource-constrained, heterogeneous fog nodes distributed across vast geographical areas and located closer to users and data sources, as compared to core cloud which is usually located at large data centers, far from users and IoT devices. To facilitate efficient deployment of services on the fog infrastructure, we propose Hierarchical and Autonomous Fog Architecture (HAFA) to organize heterogeneous fog nodes into a multi-layered connected hierarchy based on several parameters such as physical location, distance from IoT devices and/or users, node resource configuration, privacy and security. Fog nodes are grouped to facilitate resource pooling and local control, and groups of fog nodes are linked to facilitate disaster readiness and autonomy. HAFA helps reducing effort in finding an optimal node with required resource characteristics towards service deployment.

[1]  Rajkumar Buyya,et al.  Indie Fog: An Efficient Fog-Computing Infrastructure for the Internet of Things , 2017, Computer.

[2]  Xin Li,et al.  Optimal Deployment and Dimensioning of Fog Computing Supported Vehicular Network , 2016, 2016 IEEE Trustcom/BigDataSE/ISPA.

[3]  Antonio Iera,et al.  Federated edge-assisted mobile clouds for service provisioning in heterogeneous IoT environments , 2015, 2015 IEEE 2nd World Forum on Internet of Things (WF-IoT).

[4]  Marimuthu Palaniswami,et al.  EHOPES: Data-centered Fog platform for smart living , 2015, 2015 International Telecommunication Networks and Applications Conference (ITNAC).

[5]  Yong Xiang,et al.  Cost Efficient Resource Management in Fog Computing Supported Medical Cyber-Physical System , 2017, IEEE Transactions on Emerging Topics in Computing.

[6]  Vipin Kumar,et al.  Introduction to Data Mining, (First Edition) , 2005 .

[7]  Eui-nam Huh,et al.  Towards task scheduling in a cloud-fog computing system , 2016, 2016 18th Asia-Pacific Network Operations and Management Symposium (APNOMS).

[8]  Sung-Ju Lee,et al.  A Fog Operating System for User-Oriented IoT Services: Challenges and Research Directions , 2017, IEEE Communications Magazine.

[9]  Antonio Brogi,et al.  QoS-Aware Deployment of IoT Applications Through the Fog , 2017, IEEE Internet of Things Journal.

[10]  Raja Lavanya,et al.  Fog Computing and Its Role in the Internet of Things , 2019, Advances in Computer and Electrical Engineering.

[11]  Alan Davy,et al.  Resource aware placement of IoT application modules in Fog-Cloud Computing Paradigm , 2017, 2017 IFIP/IEEE Symposium on Integrated Network and Service Management (IM).

[12]  Vangelis Angelakis,et al.  Service Allocation in a Mobile Fog Infrastructure under Availability and QoS Constraints , 2018, 2018 IEEE Global Communications Conference (GLOBECOM).

[13]  Charles C. Byers,et al.  Architectural Imperatives for Fog Computing: Use Cases, Requirements, and Architectural Techniques for Fog-Enabled IoT Networks , 2017, IEEE Communications Magazine.

[14]  Xu Chen,et al.  When D2D meets cloud: Hybrid mobile task offloadings in fog computing , 2017, 2017 IEEE International Conference on Communications (ICC).

[15]  Kin K. Leung,et al.  Dynamic service migration in mobile edge-clouds , 2015, 2015 IFIP Networking Conference (IFIP Networking).

[16]  Daniel T. Larose,et al.  Discovering Knowledge in Data: An Introduction to Data Mining , 2005 .

[17]  Choong Seon Hong,et al.  An Architecture of IoT Service Delegation and Resource Allocation Based on Collaboration between Fog and Cloud Computing , 2016, Mob. Inf. Syst..