Fog Computing Applications: Taxonomy and Requirements

Ahmed, Arif; Arkian, HamidReza; Battulga, Davaadorj; Fahs, Ali J.; Farhadi, Mozhdeh; Giouroukis, Dimitrios; Gougeon, Adrien; Gutierrez, Felipe Oliveira; Pierre, Guillaume; Souza, Paulo Sérgio Lima; Tamiru, Mulugeta Ayalew; Wu, Li

doi:10.48550/arxiv.1907.11621

Cited by 6 publications

(8 citation statements)

References 54 publications

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“…As is presented in (15), there is no leader in the proposed method. This means that the leader's duty is split among all the agents, and all generators adjust their incremental cost to compensate for power mismatch.…”

Section: The Proposed Methodsmentioning

confidence: 98%

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Economic Operation of Utility-Connected Microgrids in a Fast and Flexible Framework Considering Non-Dispatchable Energy Sources

et al. 2022

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This paper introduces a modified consensus-based real-time optimization framework for utility-connected and islanded microgrids scheduling in normal conditions and under cyberattacks. The exchange of power with the utility is modeled, and the operation of the microgrid energy resources is optimized to minimize the total energy cost. This framework tracks both generation and load variations to decide optimal power generations and the exchange of power with the utility. A linear cost function is defined for the utility where the rates are updated at every time interval. In addition, a realistic approach is taken to limit the power generation from renewable energy sources, including photovoltaics (PVs), wind turbines (WTs), and dispatchable distributed generators (DDGs). The maximum output power of DDGs is limited to their ramp rates. Besides this, a specific cloud-fog architecture is suggested to make the real-time operation and monitoring of the proposed method feasible for utility-connected and islanded microgrids. The cloud-fog-based framework is flexible in applying demand response (DR) programs for more efficiency of the power operation. The algorithm’s performance is examined on the 14 bus IEEE network and is compared with optimal results. Three operating scenarios are considered to model the load as light and heavy, and after denial of service (DoS) attack to indicate the algorithm’s feasibility, robustness, and proficiency. In addition, the uncertainty of the system is analyzed using the unscented transformation (UT) method. The simulation results demonstrate a robust, rapid converging rate and the capability to track the load variations due to the probable responsive loads (considering DR programs) or natural alters of load demand.

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Section: The Proposed Methodsmentioning

confidence: 98%

“…Deploying a fog layer into the cloud-based structure is an advised solution for the desired system [14]. Cloud computing technology and its fog computing extension provide a proper environment with quick, flexible, and efficient functionality for distributed systems [15].…”

Section: Introduction 1background and Motivationmentioning

confidence: 99%

Economic Operation of Utility-Connected Microgrids in a Fast and Flexible Framework Considering Non-Dispatchable Energy Sources

et al. 2022

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“…Basically, A k can include following features: total size (in bits or bytes), splittable or non-splitable, number of data types. The sizes of input data of tasks can be ranged from kilo-bytes to tera-bytes depending the specific applications [46]. Based on this feature, the tasks can be classified into light, medium, and heavy tasks as studied in many of existing works [15], [47] for further analyzing the impact of task sizes on the performance of computation offloading approaches.…”

Section: B Computational Tasksmentioning

confidence: 99%

A Survey on Matching Theory for Distributed Computation Offloading in IoT-Fog-Cloud Systems: Perspectives and Open Issues

Tran-Dang

Kim

2022

IEEE Access

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Fog computing has been widely integrated in the IoT-based systems, creating IoT-Fog-Cloud (IFC) systems to improve the system performances and satisfy the quality of services (QoS) and quality of experience (QoE) requirements for the end users (EUs). This improvement is enabled by computational offloading schemes, which perform the task computation nearby the task generation sources (i.e., IoT devices, EUs) on behalf of remote cloud servers. To realize the benefits of offloading techniques, however, there is a need to incorporate efficient resource allocation frameworks, which can deal effectively with intrinsic properties of computing environment in the IFC systems such as resource heterogeneity of computing devices, various requirements of computation tasks, high task request rates, and so on. While the centralize optimization and non-cooperative game theory based solutions are applicable in a certain number of application scenarios, they fail to be efficient in many of cases, where the global information and control might be unavailable or cost-intensive to achieve it in the large-scale systems. The need of distributed computational offloading algorithms with low computation complexity has motivated a surge of solutions using matching theory. In the present review, we first describe the fundamental concept of this emerging tool enabling the distributed implementation in the computing environment. Then the key solution concepts and algorithmic implementations proposed in the framework of literature are highlighted and discussed. Given the powerful tool of matching theory, its full capability is still unexplored and unexploited in the literature. We thereby discover and discuss existing challenges and corresponding solutions that the matching theory can be applied to resolve them. Furthermore, new problems and open issues for application scenarios of modern IFC systems are also investigated thoroughly. INDEX TERMSIoT-Fog-Cloud systems, Matching theory, Distributed algorithm, Computational offloading. I. INTRODUCTION Practically, the Internet of Things (IoT) has become an integral element for realizing smart practical systems such as smart cities [1], smart grids [2], smart factories [3], smart logistics, and supply chain [4], [5]. The fundamental aspect of IoT-based systems is to connect all devices through the Internet protocol to exchange high volume data and pro-cess them to create smart services and applications [6], [7]. Owing to limited computation resources, network, storage, and energy, IoT devices are inadequate for executing all computational tasks, especially tasks with huge volumes and complex data structures. Cloud computing is an essential solution to this problem because it provides powerful resources to fulfill tasks efficiently [8], [9]. However, cloud

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“…Cloud functions can be moved towards the end user device in the event of low-latency interactivity. In practice, containerized microservices migrate from centralized cloud to geo-distributed fog nodes [52,66], which share the workload and lessen the network traffic. Therefore, strategies under fog perform the migration among geo-distributed and heterogeneous data centers.…”

Section: Strategies For Container Migration Techniquesmentioning

confidence: 99%

Container placement and migration strategies for cloud, fog, and edge data centers: A survey

2022

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The last decade has witnessed important development of network softwarization that has revolutionized the practice of networks. Virtualized networks bring novel and specific requirements for the control and orchestration of containerized network functions that are scattered across the network. In this regard, the migration of virtualized network functions plays a pivotal role to best meet the requirements of optimal resource utilization, load balancing and fault tolerance. The purpose of this survey is to offer a detailed overview of the progress on container migration so as to provide a better understanding of the trade-off between the benefits associated with the migration and the practical challenges. The paper includes a classification of the placement algorithms that map the containerized network functions on the virtualized infrastructure. Following, a taxonomy of the migration techniques that perform the transfer of the containerized microservices is proposed.

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Fog Computing Applications: Taxonomy and Requirements

Cited by 6 publications

References 54 publications

Economic Operation of Utility-Connected Microgrids in a Fast and Flexible Framework Considering Non-Dispatchable Energy Sources

Economic Operation of Utility-Connected Microgrids in a Fast and Flexible Framework Considering Non-Dispatchable Energy Sources

A Survey on Matching Theory for Distributed Computation Offloading in IoT-Fog-Cloud Systems: Perspectives and Open Issues

Container placement and migration strategies for cloud, fog, and edge data centers: A survey

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