Fog Computing: A Taxonomy, Survey and Future Directions

In recent years, the number of Internet of Things (IoT) devices/sensors has increased to a great extent. To support the computational demand of real-time latency-sensitive applications of largely geo-distributed IoT devices/sensors, a new computing paradigm named "Fog computing" has been introduced. Generally, Fog computing resides closer to the IoT devices/sensors and extends the Cloud-based computing, storage and networking facilities. In this chapter, we comprehensively analyse the challenges in Fogs acting as an intermediate layer between IoT devices/ sensors and Cloud datacentres and review the current developments in this field. We present a taxonomy of Fog computing according to the identified challenges and its key features.We also map the existing works to the taxonomy in order to identify current research gaps in the area of Fog computing. Moreover, based on the observations, we propose future directions for research

Engineering a QoS Provider Mechanism for Edge Computing with Deep Reinforcement Learning

With the development of new system solutions that integrate traditional cloud computing with the edge/fog computing paradigm, dynamic optimization of service execution has become a challenge due to the edge computing resources being more distributed and dynamic. How to optimize the execution to provide Quality of Service (QoS) in edge computing depends on both the system architecture and the resource allocation algorithms in place. We design and develop a QoS provider mechanism, as an integral component of a fog-to-cloud system, to work in dynamic scenarios by using deep reinforcement learning. We choose reinforcement learning since it is particularly well suited for solving problems in dynamic and adaptive environments where the decision process needs to be frequently updated. We specifically use a Deep Q-learning algorithm that optimizes QoS by identifying and blocking devices that potentially cause service disruption due to dynamicity. We compare the reinforcement learning based solution with state-of-the-art heuristics that use telemetry data, and analyze pros and cons

An SDN-based architecture for security provisioning in Fog-to-Cloud (F2C) computing systems

The unstoppable adoption of cloud and fog computing is paving the way to developing innovative services, some requiring features not yet covered by either fog or cloud computing. Simultaneously, nowadays technology evolution is easing the monitoring of any kind of infrastructure, be it large or small, private or public, static or dynamic. The fog-to-cloud computing (F2C) paradigm recently came up to support foreseen and unforeseen services demands while simultaneously benefiting from the smart capacities of the edge devices. Inherited from cloud and fog computing, a challenging aspect in F2C is security provisioning. Unfortunately, security strategies employed by cloud computing require computation power not supported by devices at the edge of the network, whereas security strategies in fog are yet on their infancy. Put this way, in this paper we propose Software Defined Network (SDN)-based security management architecture based on a master/slave strategy. The proposed architecture is conceptually applied to a critical infrastructure (CI) scenario, thus analyzing the benefits F2C may bring for security provisioning in CIs.Peer ReviewedPostprint (published version

Addressing the Challenges in Federating Edge Resources

This book chapter considers how Edge deployments can be brought to bear in a global context by federating them across multiple geographic regions to create a global Edge-based fabric that decentralizes data center computation. This is currently impractical, not only because of technical challenges, but is also shrouded by social, legal and geopolitical issues. In this chapter, we discuss two key challenges - networking and management in federating Edge deployments. Additionally, we consider resource and modeling challenges that will need to be addressed for a federated Edge.Comment: Book Chapter accepted to the Fog and Edge Computing: Principles and Paradigms; Editors Buyya, Sriram