Abstract:Cloud computing revolutionized the information technology (IT) industry by offering dynamic and infinite scaling, on-demand resources and utility-oriented usage. However, recent changes in user traffic and requirements have exposed the shortcomings of cloud computing, particularly the inability to deliver real-time responses and handle massive surge in data volumes. Fog computing, that brings back partial computation load from the cloud to the edge devices, is envisioned to be the next big change in computing,… Show more
“…Fig. [107,239,257,295,305]). A new research direction will be to design schemes that consider many objectives (e.g., QoS, bandwidth, energy, cost) simultaneously.…”
Section: Challenges and Future Research Directionsmentioning
confidence: 99%
“…The authors in [239] propose a service-oriented middleware that aims to distribute services over fog nodes for scalability, and with the help of SDN, performs QoS-aware orchestration by scheduling flows between services. The architecture proposed mainly consists of two components -the service-oriented middleware and the distributed service orchestration engine.…”
With the Internet of Things (IoT) becoming part of our daily life and our environment, we expect rapid growth in the number of connected devices. IoT is expected to connect billions of devices and humans to bring promising advantages for us. With this growth, fog computing, along with its related edge computing paradigms, such as multi-access edge computing (MEC) and cloudlet, are seen as promising solutions for handling the large volume of securitycritical and time-sensitive data that is being produced by the IoT. In this paper, we first provide a tutorial on fog computing and its related computing paradigms, including their similarities and differences. Next, we provide a taxonomy of research topics in fog computing, and through a comprehensive survey, we summarize and categorize the efforts on fog computing and its related computing paradigms. Finally, we provide challenges and future directions for research in fog computing.
“…Fig. [107,239,257,295,305]). A new research direction will be to design schemes that consider many objectives (e.g., QoS, bandwidth, energy, cost) simultaneously.…”
Section: Challenges and Future Research Directionsmentioning
confidence: 99%
“…The authors in [239] propose a service-oriented middleware that aims to distribute services over fog nodes for scalability, and with the help of SDN, performs QoS-aware orchestration by scheduling flows between services. The architecture proposed mainly consists of two components -the service-oriented middleware and the distributed service orchestration engine.…”
With the Internet of Things (IoT) becoming part of our daily life and our environment, we expect rapid growth in the number of connected devices. IoT is expected to connect billions of devices and humans to bring promising advantages for us. With this growth, fog computing, along with its related edge computing paradigms, such as multi-access edge computing (MEC) and cloudlet, are seen as promising solutions for handling the large volume of securitycritical and time-sensitive data that is being produced by the IoT. In this paper, we first provide a tutorial on fog computing and its related computing paradigms, including their similarities and differences. Next, we provide a taxonomy of research topics in fog computing, and through a comprehensive survey, we summarize and categorize the efforts on fog computing and its related computing paradigms. Finally, we provide challenges and future directions for research in fog computing.
“…We then followed the same process by applying inclusion and exclusion criteria to their titles and abstracts. As a result, we included two more papers: one peer-reviewed, and one grey literature [SP1]. The reason for including this specific non-peer-reviewed work [SP1] is due to its large amount of citations; especially when many of our selected papers referred to it as the first definition of cloud continuum.…”
The cloud continuum concept has drawn increasing attention from practitioners, academics, and funding agencies and been adopted progressively. However, the concept remains mired in various definitions with different studies providing contrasting descriptions. Therefore, to understand the concept of cloud continuum and to provide its definition, in this work we conduct a systematic mapping study of the literature investigating the different definitions, how they evolved, and where does the cloud continue. The main outcome of this work is a complete definition that merges all the common aspects of cloud continuum, which enables practitioners and researchers to better understand what cloud continuum is.INDEX TERMS Cloud continuum, edge, Fog.
“…Vilalta et al [43] proposed a new fog computing infrastructure named TelcoFog that can be installed at the edge of the mobile network of the telecom operator to provide several services, such as NFV (Network Function Virtualization) and MEC for IoT applications, the benefits of the proposed infrastructure are dynamic deployment, scalability, and low latency. Gupta et al [44] proposed a highly distributed service-oriented middleware called SDFog (Software-Defined Fog) based on cloud and fog capabilities as well as SDN (Software-Defined Networking) and NFV to satisfy the required high level of scalability and QoS.…”
Section: A General Review Of Iot-based Systems' Requirementsmentioning
Adopting new information and communication technology (ICT) as a solution to achieve food security becomes more urgent than before, particularly with the demographical explosion. In this survey, we analyze the literature in the last decade to examine the existing fog/edge computing architectures adapted for the smart farming domain and identify the most relevant challenges resulting from the integration of IoT and fog/edge computing platforms. On the other hand, we describe the status of Blockchain usage in intelligent farming as well as the most challenges this promising topic is facing. The relevant recommendations and researches needed in Blockchain topic to enhance intelligent farming sustainability are also highlighted. It is found through the examination that the adoption of ICT in the various farming processes helps to increase productivity with low efforts and costs. Several challenges are faced when implementing such solutions, they are mainly related to the technological development, energy consumption, and the complexity of the environments where the solutions are implemented. Despite these constraints, it is certain that shortly several farming businesses will heavily invest to introduce more intelligence into their management methods. Furthermore, the use of sophisticated deep learning and Blockchain algorithms may contribute to the resolution of many recent farming issues.
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