A Framework of Fog Computing: Architecture, Challenges, and Optimization

Liu, Yang; Fieldsend, Jonathan E.; Min, Geyong

doi:10.1109/access.2017.2766923

Cited by 182 publications

(101 citation statements)

References 26 publications

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“…In the case with only one RAT interface (there is no AQUAplus algorithm within the mobile nodes and when we are using the basic mobile fog and cloud computing methods [15][16][17][18]) the average queue delay values are maximal (see the curve where M=1 in Figure 3). Also, with smaller number of RAT interfaces the average throughput is smaller and is supporting only those RAT interfaces belonging to the RAT with best mobility support.…”

Section: Simulation Results and Analysismentioning

confidence: 99%

“…It is emphasized that in Figures 2-4 the case when there is only one used interface (M=1) is the case when we are using the basic mobile fog and cloud computing methods [15][16][17][18] and there is no AQUAplus algorithm within the used mobile nodes. With this specific comparison, the novelty and actual value of the proposed framework is clearly defined.…”

Section: Simulation Results and Analysismentioning

confidence: 99%

“…Then, the guaranteed battery lifetime ( bat ) for the proposed 5G mobile node can be found by the derivation from the direct dependency of the total energy and the total battery power, i.e., from the starting point ( ) = (18) and from the fact that, for a particular radio interface m, the total energy on the m-th interface is…”

Section: Theorem 1 For Each Positive Number T Which Is the Number Omentioning

confidence: 99%

“…In order to provide seamless extension of cloud computing into the edge of the network for secure control and management of domain specific hardware, software, and standard computing, storage, and network functions within the domain and enable secure rich data processing applications across the domain, a new paradigm known as fog computing is introduced [15][16][17][18]. It represents a completely geodistributed network, multilayered cloud computing architecture for data processing and storage, which contains billions of devices as part of Internet of Things (IoT), multiple local clouds at the edge of the network: fog and main central hyperscalable cloud computing data center.…”

Section: Related Workmentioning

confidence: 99%

“…Fog distributes computing, data processing, and networking services to the edge of the network, closer to end users. It is an architecture where distributed edge and user devices collaborate with each 2 Wireless Communications and Mobile Computing other and with the clouds to carry out computing, control, networking, and data management tasks [17,18].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Advanced QoS Provisioning and Mobile Fog Computing for 5G

Shuminoski

Kitanov

Janevski

2018

Wireless Communications and Mobile Computing

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This paper presents a novel QoS and mobile cloud and fog computing framework for future fifth generation (5G) of mobile and fixed nodes with radio network aggregation capability. The proposed 5G framework is leading to high QoS provisioning for any given multimedia service, higher bandwidth utilization, traffic load sharing, mobile cloud plus fog computing features, and multi-radio interface capabilities. The framework is user-centric, targeted at always-on connectivity with using radio network aggregation for available mobile broadband connections, and empowered with mobile cloud and fog computing advantages. Moreover, our proposed framework is using Lyapunov drift-plus-penalty theorem that provides a methodology for designing algorithm to maximize the average throughput and stabilize the queuing. Also, we are showing the upper bound of the consumed power and the lower bound of the battery lifetime for the proposed 5G terminal. The advanced performance of our 5G QoS plus MCC framework is evaluated using simulations and analysis with multimedia traffic in heterogeneous mobile and wireless environment. The simulation results are showing that the maximal network utilization, maximal throughput, minimal end-to-end delay, efficient energy consumption, and other performance improvements are achieved.

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Section: Simulation Results and Analysismentioning

confidence: 99%