Fog Computing: A Taxonomy, Survey and Future Directions

Mahmud, Redowan; Ramamohanarao, Kotagiri; Buyya, Rajkumar

doi:10.1007/978-981-10-5861-5_5

Cited by 690 publications

(427 citation statements)

References 46 publications

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“…There are many other good surveys for MEC and edge computing from various points of view, such as Refs. [80], [81] and [82], among others. The security implication of MEC, which we did not mentioned, are surveyed in Ref.…”

Section: Resultsmentioning

confidence: 99%

Multi-access Edge Computing: A Survey

Tanaka

Yoshida

Möri

et al. 2018

Journal of Information Processing

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Multi-access Edge Computing (MEC) can be defined as a model for enabling business oriented, cloud computing platform within multiple types of the access network (e.g., LTE, 5G, WiFi, FTTH, etc.) at the close proximity of subscribers to serve delay sensitive, context aware applications. To pull out the most of the potential, MEC has to be designed as infrastructure, to support many kind of IoT applications and their eco system, in addition to sufficiently management mechanism. In this context, various research and standardization efforts are ongoing. This paper provides a comprehensive survey of the state-of-the-art research efforts on MEC domain, with focus on the architectural proposals as infrastracture, the issue of the partitioning of processing among the user devices, edge servers, and a cloud, and the issue of the resource management.

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Section: Resultsmentioning

confidence: 99%

Multi-access Edge Computing: A Survey

Tanaka

Yoshida

Möri

et al. 2018

Journal of Information Processing

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“…Therefore we now have the emerging hybrid paradigm of Fog computing [4], [70]. In order for this paradigm to successfully achieve integration of these systems the reliability of every aspect must be managed and guaranteed with a level of Quality of Service (QoS).…”

Section: Challenge: Computational Systemsmentioning

confidence: 99%

“…The Fog paradigm of providing a virtual layer between the data centre and the IoT devices must be extended to provide a virtual cloud that hides the identity or location of all data centers, but also encapsulates other compute resources such HPC facilities, as shown in Figure 2. Such an approach could be used to mitigate the issues of scalability, fault-tolerance, elasticity [2] as well as facilitating management services to detect failures [70].…”

Section: Challenge: Computational Systemsmentioning

confidence: 99%

Massive-Scale Automation in Cyber-Physical Systems: Vision & Challenges

McKee

Clement

Almutairi

et al. 2017

2017 IEEE 13th International Symposium on Autonomous Decentralized System (ISADS)

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Abstract-The next era of computing is the evolution of the Internet of Things (IoT) and Smart Cities with development of the Internet of Simulation (IoS). The existing technologies of Cloud, Edge, and Fog computing as well as HPC being applied to the domains of Big Data and deep learning are not adequate to handle the scale and complexity of the systems required to facilitate a fully integrated and automated smart city. This integration of existing systems will create an explosion of data streams at a scale not yet experienced. The additional data can be combined with simulations as services (SIMaaS) to provide a shared model of reality across all integrated systems, things, devices, and individuals within the city. There are also numerous challenges in managing the security and safety of the integrated systems. This paper presents an overview of the existing stateof-the-art in automating, augmenting, and integrating systems across the domains of smart cities, autonomous vehicles, energy efficiency, smart manufacturing in Industry 4.0, and healthcare. Additionally the key challenges relating to Big Data, a model of reality, augmentation of systems, computation, and security are examined.

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“…At the same time, with the wide adoption of IoT services, a variety of household appliances and sensors will be connected to the Internet and produce a large amount of data [3][4][5]. It has been estimated that the number of devices connected by IoT will reach 50 billion to 100 billion by 2020, which means there will be more and more data without the control of existing techniques on data processing and analysis, privacy leaks may be caused, and the quality of service will be decreased [6,7].…”

Section: Introductionmentioning

confidence: 99%

Predicting Short‐Term Electricity Demand by Combining the Advantages of ARMA and XGBoost in Fog Computing Environment

Zheng

Yang

et al. 2018

Wireless Communications and Mobile Computing

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With the rapid development of IoT, the disadvantages of Cloud framework have been exposed, such as high latency, network congestion, and low reliability. Therefore, the Fog Computing framework has emerged, with an extended Fog Layer between the Cloud and terminals. In order to address the real-time prediction on electricity demand, we propose an approach based on XGBoost and ARMA in Fog Computing environment. By taking the advantages of Fog Computing framework, we first propose a prototypebased clustering algorithm to divide enterprise users into several categories based on their total electricity consumption; we then propose a model selection approach by analyzing users' historical records of electricity consumption and identifying the most important features. Generally speaking, if the historical records pass the test of stationarity and white noise, ARMA is used to model the user's electricity consumption in time sequence; otherwise, if the historical records do not pass the test, and some discrete features are the most important, such as weather and whether it is weekend, XGBoost will be used. The experiment results show that our proposed approach by combining the advantage of ARMA and XGBoost is more accurate than the classical models.

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Fog Computing: A Taxonomy, Survey and Future Directions

Cited by 690 publications

References 46 publications

Multi-access Edge Computing: A Survey

Multi-access Edge Computing: A Survey

Massive-Scale Automation in Cyber-Physical Systems: Vision & Challenges

Predicting Short‐Term Electricity Demand by Combining the Advantages of ARMA and XGBoost in Fog Computing Environment

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