404 research outputs found

    Fog Computing: A Taxonomy, Survey and Future Directions

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    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

    Do we all really know what a fog node is? Current trends towards an open definition

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    Fog computing has emerged as a promising technology that can bring cloud applications closer to the physical IoT devices at the network edge. While it is widely known what cloud computing is, how data centers can build the cloud infrastructure and how applications can make use of this infrastructure, there is no common picture on what fog computing and particularly a fog node, as its main building block, really is. One of the first attempts to define a fog node was made by Cisco, qualifying a fog computing system as a “mini-cloud” located at the edge of the network and implemented through a variety of edge devices, interconnected by a variety, mostly wireless, communication technologies. Thus, a fog node would be the infrastructure implementing the said mini-cloud. Other proposals have their own definition of what a fog node is, usually in relation to a specific edge device, a specific use case or an application. In this paper, we first survey the state of the art in technologies for fog computing nodes, paying special attention to the contributions that analyze the role edge devices play in the fog node definition. We summarize and compare the concepts, lessons learned from their implementation, and end up showing how a conceptual framework is emerging towards a unifying fog node definition. We focus on core functionalities of a fog node as well as in the accompanying opportunities and challenges towards their practical realization in the near future.Postprint (author's final draft

    Towards a proper service placement in combined Fog-to-Cloud (F2C) architectures

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    The Internet of Things (IoT) has empowered the development of a plethora of new services, fueled by the deployment of devices located at the edge, providing multiple capabilities in terms of connectivity as well as in data collection and processing. With the inception of the Fog Computing paradigm, aimed at diminishing the distance between edge-devices and the IT premises running IoT services, the perceived service latency and even the security risks can be reduced, while simultaneously optimizing the network usage. When put together, Fog and Cloud computing (recently coined as fog-to-cloud, F2C) can be used to maximize the advantages of future computer systems, with the whole greater than the sum of individual parts. However, the specifics associated with cloud and fog resource models require new strategies to manage the mapping of novel IoT services into the suitable resources. Despite few proposals for service offloading between fog and cloud systems are slowly gaining momentum in the research community, many issues in service placement, both when the service is ready to be executed admitted as well as when the service is offloaded from Cloud to Fog, and vice-versa, are new and largely unsolved. In this paper, we provide some insights into the relevant features about service placement in F2C scenarios, highlighting main challenges in current systems towards the deployment of the next-generation IoT servicesPostprint (author's final draft

    Serverless computing for the Internet of Things

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    Cloud-based services have evolved significantly over the years. Cloud computing models such as IaaS, PaaS and SaaS are serving as an alternative to traditional in-house infrastructure-based approach. Furthermore, serverless computing is a cloud computing model for ephemeral, stateless and event-driven applications that scale up and down instantly. In contrast to the infinite resources of cloud computing, the Internet of Things is the network of resource-constrained, heterogeneous and intelligent devices that generate a significant amount of data. Due to the resource-constrained nature of IoT devices, cloud resources are used to process data generated by IoT devices. However, data processing in the cloud also has few limitations such as latency and privacy concerns. These limitations arise a requirement of local processing of data generated by IoT devices. A serverless platform can be deployed on a cluster of IoT devices using software containers to enable local processing of the sensor data. This work proposes a hybrid multi-layered architecture that not only establishes the possibility of local processing of sensor data but also considers the issues such as heterogeneity, resource constraint nature of IoT devices. We use software containers, and multi-layered architecture to provide the high availability and fault tolerance in our proposed solution

    Cloudlet computing : recent advances, taxonomy, and challenges

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    A cloudlet is an emerging computing paradigm that is designed to meet the requirements and expectations of the Internet of things (IoT) and tackle the conventional limitations of a cloud (e.g., high latency). The idea is to bring computing resources (i.e., storage and processing) to the edge of a network. This article presents a taxonomy of cloudlet applications, outlines cloudlet utilities, and describes recent advances, challenges, and future research directions. Based on the literature, a unique taxonomy of cloudlet applications is designed. Moreover, a cloudlet computation offloading application for augmenting resource-constrained IoT devices, handling compute-intensive tasks, and minimizing the energy consumption of related devices is explored. This study also highlights the viability of cloudlets to support smart systems and applications, such as augmented reality, virtual reality, and applications that require high-quality service. Finally, the role of cloudlets in emergency situations, hostile conditions, and in the technological integration of future applications and services is elaborated in detail. © 2013 IEEE
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