On the Design of Computation Offloading in Fog Radio Access Networks

Zhao, Zhongyuan; Bu, Shuqing; Zhao, Tiezhu; Yin, Zhenping; Ding, Zhiguo; Quek, Tony Q. S.

doi:10.1109/tvt.2019.2919915

Cited by 80 publications

(37 citation statements)

References 39 publications

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“…In online deployment, the decision of computational offloading takes place at run-time and considers the current system status and process characteristics, such as the current waiting time. Most research investigating the offloading problem have considered the offline approach [4,8,9,14,15,25] , while online approach has limited coverage [11][12][13]16]. This shed lights on the importance of investigating the online computational offloading method, our approach primarily makes use of the online approach.…”

Section: Resultsmentioning

confidence: 99%

“…Offloading computing tasks to a cloud data centre and/or neighbouring fog computing servers has received considerable attention in other publications [4,8,9,[12][13][14][15]. Due to the differences in system models, existing works can be categorised as outlined below:…”

Section: Computational Offloadingmentioning

confidence: 99%

“…This leads to fewer tasks being processed within the fog environment, thereby reducing the energy consumed in fog nodes. Computational offloading has been studied in fog radio access networks [13] to achieve minimum system cost, which is the weighted sum of total energy consumption and total offloading latency. In their work, task latency only involves computation and transmission latencyno queuing latency is considered.…”

Section: Computation Offloading In Iot-fog-cloud Computing Systemsmentioning

confidence: 99%

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Minimising Delay and Energy in Online Dynamic Fog Systems

Alenizi¹,

Rana²

2020

Computer Science &Amp; Information Technology (CS &Amp; IT)

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The increasing use of Internet of Things (IoT) devices generates a greater demand for data transfers and puts increased pressure on networks. Additionally, connectivity to cloud services can be costly and inefficient. Fog computing provides resources in proximity to user devices to overcome these drawbacks. However, optimisation of quality of service (QoS) in IoT applications and the management of fog resources are becoming challenging problems. This paper describes a dynamic online offloading scheme in vehicular traffic applications that require execution of delay-sensitive tasks. This paper proposes a combination of two algorithms: dynamic task scheduling (DTS) and dynamic energy control (DEC) that aim to minimise overall delay, enhance throughput of user tasks and minimise energy consumption at the fog layer while maximising the use of resource-constrained fog nodes. Compared to other schemes, our experimental results show that these algorithms can reduce the delay by up to 80.79% and reduce energy consumption by up to 66.39% in fog nodes. Additionally, this approach enhances task execution throughput by 40.88%.

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Section: Resultsmentioning

confidence: 99%

Section: Computational Offloadingmentioning

confidence: 99%

Section: Computation Offloading In Iot-fog-cloud Computing Systemsmentioning

confidence: 99%

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Minimising Delay and Energy in Online Dynamic Fog Systems

Alenizi¹,

Rana²

2020

Computer Science &Amp; Information Technology (CS &Amp; IT)

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“…We assume that each MTCD generates W task v , which needs to be offloaded within the allocated time frame either at the edge tier or cloud-tier, based on the task size and offloading urgency. With transmission delay D v,M from MTCD v to cloud-tier M and computing delay D computing edge at the edge node, total delay from MTCD to cloud-tier is considered as [27];…”

Section: B Energy Consumption Modelmentioning

confidence: 99%

“…Furthermore, φ j,v represents the maximum available computing resource at the edge. Similarly, the energy consumption from uploading the data from MTCD v to cloud-tier M including processing at the edge and cloud tiers can be represented as [27];…”

Section: B Energy Consumption Modelmentioning

confidence: 99%

Advanced Energy-Efficient Computation Offloading Using Deep Reinforcement Learning in MTC Edge Computing

et al. 2020

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Mobile edge computing (MEC) supports the internet of things (IoT) by leveraging computation offloading. It minimizes the delay and consequently reduces the energy consumption of the IoT devices. However, the consideration of static communication mode in most of the recent work, despite varying network dynamics and resource diversity, is the main limitation. An energy-efficient computation offloading method using deep reinforcement learning (DRL) is proposed. Both delay-tolerant and non-delay tolerant scenarios are considered using capillary machine type communication (MTC). Depending upon the type of service, an intelligent MTC edge server using DRL decides either process the incoming request at the MTC edge server or sends it to the cloud server. To control communication, we draft a markov decision problem (MDP). This minimizes the long-term power consumption of the system. The formulation of the optimization problem is considered under the constraint of computing power resources and delays. Simulation results delineate the significant performance gain of 12% in computation offloading through the proposed DRL approach. The effectiveness and superiority of the proposed model are compared with other baselines and are demonstrated numerically. INDEX TERMS Machine type communication, mobile edge computing, computation offloading, deep reinforcement learning, energy efficiency.

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Federated Learning with Unreliable Transmission in Mobile Edge Computing Systems

Feng,

Zhao

et al. 2023

Federated Learning for Future Intelligent Wireless Networks

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On the Design of Computation Offloading in Fog Radio Access Networks

Cited by 80 publications

References 39 publications

Minimising Delay and Energy in Online Dynamic Fog Systems

Minimising Delay and Energy in Online Dynamic Fog Systems

Advanced Energy-Efficient Computation Offloading Using Deep Reinforcement Learning in MTC Edge Computing

Federated Learning with Unreliable Transmission in Mobile Edge Computing Systems

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