Task Offloading and Scheduling in Fog RAN: A Parallel Communication and Computation Perspective

Guo, Kun; Sheng, Min; Quek, Tony Q. S.; Qiu, Zhiliang

doi:10.1109/lwc.2019.2948860

Cited by 41 publications

(16 citation statements)

References 11 publications

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“…Some literature proposes energy-efficient resource management schemes for coordination computation, such as offloading among fog computing devices. In [14], the authors addressed the computation offloading mechanism for fog Radio Access Network (RAN) along with a task offloading algorithm is proposed based on parallel communication and computation for radio access network claiming steady scalability and efficiency. [15] also considered a 5G fog radio access network to tackle offloading decision challenge and minimize energy and delay.…”

Section: Related Workmentioning

confidence: 99%

ioFog: Prediction-based Fog Computing Architecture for Offline IoT

Alam¹,

Ahmed²,

Matam³

et al. 2021

Preprint

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<div>Due to the multi-hop, long-distance, and wireless backbone connectivity, provisioning critical and diverse services face challenges such as low latency and reliability. This paper proposes ioFog, an offline fog architecture for achieving reliability and low latency in a large backbone network. Our solution uses a Markov chain-based task prediction model to offer dynamic service requirements with minimal dependency on the Internet. The proposed architecture considers a central Fog Controller (FC) to (i) provide a global status overview and (ii) predict upcoming tasks of Fog Nodes for intelligent offloading decisions. The FC also has the current status of the existing fog nodes in terms of their processing and storage capabilities. Accordingly, it can schedule the possible future offline computations and task allocations. ioFog considers the requirements of individual IoT applications and enables improved fog computing decisions. As compared to the existing offline IoT solutions, ioFog improves service time significantly and service delivery ratio up to 23%.</div>

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Section: Related Workmentioning

confidence: 99%

ioFog: Prediction-based Fog Computing Architecture for Offline IoT

Alam¹,

Ahmed²,

Matam³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…There have been lots of works on MEC offloading problems from the perspectives of improving energy efficiency [4][5][6][7][8][9], shortening system delay [10][11][12][13][14][15], and balancing the two objectives [16,17]. [4] proposed an energy-effective resource allocation strategy based on wireless transmission energy in a two-user MEC system.…”

Section: Introductionmentioning

confidence: 99%

Latency Optimization for D2D-Enabled Parallel Mobile Edge Computing in Cellular Networks

Cai

Liang

Zhang

et al. 2021

Preprint

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Edge offloading, including offloading to edge base stations (BS) via cellular links and to idle mobile users (MUs) via device-to-device (D2D) links, has played a vital role in achieving ultra-low latency characteristics in 5G wireless networks. This paper studies an offloading method of parallel communication and computation to minimize the delay in multi-user systems. Three different scenarios are explored, i.e., full offloading, partial offloading, and D2D-enabled partial offloading. In the full offloading scenario, we find a serving order for the MUs. Then, we jointly optimize the serving order and task segment in the partial offloading scenario. For the D2D-enabled partial offloading scenario, we decompose the problem into two subproblems and then find the optimal solution based on the results of the two subproblems. Finally, the simulation results demonstrate that the offloading method of parallel communication and computing can significantly reduce the system delay, and the D2D-enabled partial offloading can further reduce the latency.

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“…There have been lots of works on MEC offloading problems from the perspectives of improving energy efficiency [4][5][6][7][8][9], shortening system delay [10][11][12][13][14][15], and balancing the two objectives [16,17]. Ji and Guo [4] proposed an energy-effective resource allocation strategy based on wireless transmission energy in a two-user MEC system.…”

Section: Introductionmentioning

confidence: 99%

Latency optimization for D2D-enabled parallel mobile edge computing in cellular networks

Cai

Liang

Zhang

et al. 2021

J Wireless Com Network

View full text Add to dashboard Cite

Edge offloading, including offloading to edge base stations (BS) via cellular links and to idle mobile users (MUs) via device-to-device (D2D) links, has played a vital role in achieving ultra-low latency characteristics in 5G wireless networks. This paper studies an offloading method of parallel communication and computation to minimize the delay in multi-user systems. Three different scenarios are explored, i.e., full offloading, partial offloading, and D2D-enabled partial offloading. In the full offloading scenario, we find a serving order for the MUs. Then, we jointly optimize the serving order and task segment in the partial offloading scenario. For the D2D-enabled partial offloading scenario, we decompose the problem into two subproblems and then find the sub-optimal solution based on the results of the two subproblems. Finally, the simulation results demonstrate that the offloading method of parallel communication and computing can significantly reduce the system delay, and the D2D-enabled partial offloading can further reduce the latency.

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Task Offloading and Scheduling in Fog RAN: A Parallel Communication and Computation Perspective

Cited by 41 publications

References 11 publications

ioFog: Prediction-based Fog Computing Architecture for Offline IoT

ioFog: Prediction-based Fog Computing Architecture for Offline IoT

Latency Optimization for D2D-Enabled Parallel Mobile Edge Computing in Cellular Networks

Latency optimization for D2D-enabled parallel mobile edge computing in cellular networks

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