All one needs to know about fog computing and related edge computing paradigms: A complete survey

Internet of Things (IoT): A review of enabling technologies, challenges, and open research issues

Edge computing technologies for Internet of Things: a primer

The IoT for smart sustainable cities of the future: An analytical framework for sensor-based big data applications for environmental sustainability

Emerging technologies such as the Internet of Things (IoT) require latency-aware computation for real-time application processing. In IoT environments, connected things generate a huge amount of data, which are generally referred to as big data. Data generated from IoT devices are generally processed in a cloud infrastructure because of the on-demand services and scalability features of the cloud computing paradigm. However, processing IoT application requests on the cloud exclusively is not an efficient solution for some IoT applications, especially time-sensitive ones. To address this issue, Fog computing, which resides in between cloud and IoT devices, was proposed. In general, in the Fog computing environment, IoT devices are connected to Fog devices. These Fog devices are located in close proximity to users and are responsible for intermediate computation and storage. One of the key challenges in running IoT applications in a Fog computing environment are resource allocation and task scheduling. Fog computing research is still in its infancy, and taxonomy-based investigation into the requirements of Fog infrastructure, platform, and applications mapped to current research is still required. This survey will help the industry and research community synthesize and identify the requirements for Fog computing. This paper starts with an overview of Fog computing in which the definition of Fog computing, research trends, and the technical differences between Fog and cloud are reviewed. Then, we investigate numerous proposed Fog computing architectures and describe the components of these architectures in detail. From this, the role of each component will be defined, which will help in the deployment of Fog computing. Next, a taxonomy of Fog computing is proposed by considering the requirements of the Fog computing paradigm. We also discuss existing research works and gaps in resource allocation and scheduling, fault tolerance, simulation tools, and Fog-based microservices. Finally, by addressing the limitations of current research works, we present some open issues, which will determine the future research direction for the Fog computing paradigm.

Fog Computing: Survey of Trends, Architectures, Requirements, and Research Directions

Fog computing aims at extending the Cloud by bringing computational power, storage, and communication capabilities to the edge of the network, in support of the IoT. Segmentation, distribution, and adaptive deployment of functionalities over the continuum from Things to Cloud are challenging tasks, due to the intrinsic heterogeneity, hierarchical structure, and very large scale infrastructure they will have to exploit. In this paper, we propose a simple, yet general, model to support the QoS-aware deployment of multicomponent IoT applications to Fog infrastructures. The model describes operational systemic qualities of the available infrastructure (latency and bandwidth), interactions among software components and Things, and business policies. Algorithms to determine eligible deployments for an application to a Fog infrastructure are presented. A Java tool, FogTorch , based on the proposed model has been prototyped.

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QoS-Aware Deployment of IoT Applications Through the Fog

Deploying composite applications to Fog nodes in a QoS-and context-aware manner is challenging due to the heterogeneity and scale of Fog infrastructures. Application components must be provided with the software and hardware capabilities they need. Communication links that support interactions between components must meet certain QoS (latency and bandwidth). On the other hand, different Fog and Cloud nodes provide different software and hardware capabilities, and actual communication links support different QoS over time. In this paper we present a prototype (FogTorchII) capable of determining deployments of composite applications to Fog infrastructures, which fulfil software, hardware and QoS requirements. FogTorchII exploits Monte Carlo simulations to take into account possible variations of the QoS of communication links. It classifies eligible deployments both in terms of QoS-assurance and of Fog resource consumption. We illustrate the utility of FogTorchII over a motivating example where we compare different possible deployments for a smart agriculture application.

How to Best Deploy Your Fog Applications, Probably

Deploying IoT applications through the Fog in a QoS-, context-, and cost-aware manner is challenging due to the heterogeneity, scale and dynamicity of Fog infrastructures. To decide how to allocate app functionalities over the continuum from the IoT to the Cloud, app administrators need to find a trade-off among QoS, resource consumption and cost. In this paper, we present a novel cost model for estimating the cost of deploying IoT applications to Fog infrastructures. We show how the inclusion of the cost model in the FogTorchΠ open-source prototype permits to determine eligible deployments of multi-component applications to Fog infrastructures and to rank them according to their QoS-assurance, Fog resource consumption and cost. We run the extended prototype on a motivating scenario, showing how it can support IT experts in choosing the deployments that best suit their desiderata.

Deploying Fog applications How much does it cost, by the way?

As for every new technological trend, education of new scientists and engineers will be instrumental in shaping the implementation of the Fog architecture, including software development. In this paper, we describe a 2-hour hands-on lab activity that we designed and run during an M.Sc. course at the Department of Computer Science of the University of Pisa, Italy. The proposed activity is designed to practically (and incrementally) show different deployment models for IoT applications — viz., IoT+Edge, IoT+Cloud and Fog — over a meaningful yet simple application example. In addition to featuring a quick learning curve and limited cost, the proposed activity shows some possible extensions to design other lab sessions.

Bonsai in the Fog: An active learning lab with Fog computing

Abstract Developing and releasing multiservice applications rely upon a pipeline of automation tools known as Continuous Integration/Continuous Deployment. Among those tools, continuous reasoning is exploited by large companies to perform incremental static analyses on their code commits as soon as they are integrated into a shared codebase. In this article, we extend continuous reasoning towards the continuous QoS- and context-aware management of multiservice applications in Cloud-IoT scenarios. We propose a novel continuous reasoning methodology that supports runtime decision on service placement by reacting both to changes in the infrastructure and in the application requirements, and capable of suggesting migrations only for services affected by such changes. The methodology is prototyped in Prolog and assessed through simulations over a realistic use case and over a lifelike motivating scenario at increasing infrastructure sizes. Experimental results show that our approach brings considerable speed-up in comparison with an exhaustive search employing non-incremental reasoning. 

Stefano Forti

Papers

QoS-Aware Deployment of IoT Applications Through the Fog

How to Best Deploy Your Fog Applications, Probably

Deploying Fog applications How much does it cost, by the way?

Bonsai in the Fog: An active learning lab with Fog computing

Declarative continuous reasoning in the cloud-IoT continuum