A Fog Computing Based Architecture for IoT Services and Applications Development

Abuseta, Yousef

doi:10.14445/22312803/ijctt-v67i10p116

Cited by 12 publications

(6 citation statements)

References 13 publications

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“…While in Centralized placement, a central control loop is deployed either on the fog computing or cloud platform (depending on the application scale) to regulate the operation of the different control loops at the same level. [27].…”

Section: Resource Allocation Approaches In Fog Computingmentioning

confidence: 99%

“…[26]. While in decentralized placement, a set of control loops of the same level is coordinated to accomplish the four activities (monitoring, analysis, planning and execution) [27]. At last, the last essential classification of our taxonomy is Integrated allocation, where hybrid load balancing provides a trade-off between the advantages of both decentralized and centralized solutions.…”

Section: Resource Allocation Approaches In Fog Computingmentioning

confidence: 99%

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Resource Allocation in Fog Computing: A Systematic Review

Salem¹,

Algaphari²

2023

JST

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Despite the importance of resource allocation issues, there is no systematic, comprehensive and detailed survey on resource allocation approaches in the fog-computing context. In this article, we provide a Systematic Literature Review (SLR) on the resource allocation approaches in fog environments in the form of a classical taxonomy to recognize the state-of-the-art mechanisms on this important topic and provide open issues. The presented taxonomy is categorized into three main fields: centralized allocation, decentralized allocation, and integrated allocation (published between 2017 and March 2022). According to what is known in fog computing, load balancing and service placement are among the most important basic parameters that ensure service quality. These fields are classified into four methods, approximate, exact, fundamental, and hybrid. In addition, this article investigates resource allocation metrics with all advantages and limitations related to chosen resource allocation mechanisms in networks.

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Section: Resource Allocation Approaches In Fog Computingmentioning

confidence: 99%

Section: Resource Allocation Approaches In Fog Computingmentioning

confidence: 99%

Resource Allocation in Fog Computing: A Systematic Review

Salem¹,

Algaphari²

2023

JST

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“…Fog Computing [25] is a computing platform with wireless distribution that allows a collection of shared resources at the IoT gateway level in a specific place to accomplish sophisticated latency-sensitive activities. According to another description [26], fog computing is architecture of horizontal system that extends storage, control, compute, and networking capabilities closer to users beside a cloud-to-device continuum. Whereas under the first definition, processing and storage capabilities are distributed across a large number of IoT devices that are placed alongside layer devices.…”

Section: Fog Computing Architecturementioning

confidence: 99%

Hybrid Load Balancing Algorithm for Fog Computing Environment

Abuhamdah¹,

Al-Shabi²

2022

IJSECS

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Fog computing has become a new trend in the Internet of things domain and cloud computing applications. It is a novel model to achieve the availability, flexibility and better responding time. In spite of that, there is so many challenges facing computing environments such as the misuse of the resources and load-balancing between them, which has a major effect on performance. The requirement of effective and robust load-balancing algorithms is one of the most significant interest in this field. Many researchers suggested various load-balancing algorithms in fog computing, but there is still inefficiency in the system performance and misalignment in load -balancing. This paper will provide a description of numerous concepts such as computing fog, fog nodes, load balancing and then we recommend a load-balancing algorithm to enhance the fog-computing environment performance, which is a hybrid algorithm benefits from the optimizing processing time (OPT) algorithms. In order to explore the proposed algorithm performance, a comparison made with other algorithms. Results indicates that using the proposed optimizing processing time algorithm in load-balancing algorithm has superior response and processing time than the compared algorithms to user requests, and the data total cost centre’s as well.

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“…These reviews cover open issues, architecture, benefits, implementation challenges, applications and future research directions. The detail FC architecture for IoT applications and services, their evaluation, and future research directions about FC architecture is presented in [58][59][60][61]. There are a number of challenges relating FC which includes general challenges [54,55], data aggregation challenge [62] and security challenges [56,63,64].…”

Section: Literature Surveymentioning

confidence: 99%

Software-Defined Dew, Roof, Fog and Cloud (SD-DRFC) Framework for IoT Ecosystem: The Journey, Novel Framework Architecture, Simulation, and Use Cases

2021

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The Internet of Things (IoT) has become one of the most known terms in present times, reaching new levels and setting a trend in the world. Evidently, it is the future of connectivity which has turned physical objects into intelligent objects. Therefore, there has been a growing curiosity in the IoT field and this leads to the concept of IoT ecosystem. But the contemporary fragmented ecosystem of regulations, technologies, and systems slows IoT deployments. Thus, we consider multi-tiered computational infrastructure which would be feasible to provide services from the nearest possible location of end devices. To mitigate multi-tier infrastructure issues, Software-Defined Networking (SDN) steps in. The journey behind SDN-supported multi-tier computational infrastructure can be understood from this paper's elaborate study. Next, a comparative analysis on dew, roof, fog and cloud is conducted and the impact of internet on these computing paradigms is briefly explained. Then a novel framework termed "SD-DRFC (Software-Defined Dew, Roof, Fog and Cloud computing)" is proposed for today's IoT ecosystem. The role and functionality of each tier of SD-DRFC framework are adequately explained. A use case focused on the SD-DRFC framework is then presented and simulated by utilizing iFogSim simulator. To evaluate the efficiency of the presented SD-DRFC framework, four QoS parameters (Latency, Network Usage, Cost, and Energy Consumption) are considered in this paper. When comparing the simulation results, the presented SD-DRFC framework performs much better than cloud-only implementation. The advantages and suitability of utilizing this proposed framework have been demonstrated by multiple use-cases which range from conceptual visions to existing running systems.

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A Fog Computing Based Architecture for IoT Services and Applications Development

Cited by 12 publications

References 13 publications

Resource Allocation in Fog Computing: A Systematic Review

Resource Allocation in Fog Computing: A Systematic Review

Hybrid Load Balancing Algorithm for Fog Computing Environment

Software-Defined Dew, Roof, Fog and Cloud (SD-DRFC) Framework for IoT Ecosystem: The Journey, Novel Framework Architecture, Simulation, and Use Cases

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