Latency minimization for multiuser computation offloading in fog-radio access networks

Li, Qiang; Liu, Jin; Lin, Jie; Wu, Xiaoxiao

doi:10.1016/j.dcan.2023.05.011

Cited by 2 publications

(1 citation statement)

References 63 publications

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“…In [12], the author Zhang et al, (2023) explored noncooperative and cooperative offloading methods for a multiuser computational off-loading issue in a Fog-Radio Access Network (F-RAN). To compute all UE jobs quickly, the maximum latency of all User Equipment (UE) is minimized by simultaneously optimizing UE-Fog connection, radio, and F-RAN computing streams..…”

Section: Related Workmentioning

confidence: 99%

Optimization based Data Aggregation and Fault Tolerance with Energy Management for Fog Enabled Heterogeneous IoT Environment

Nalayini,

jenifer,

Meena

2024

Preprint

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Fog computing is one of the efficient technologies used for overcoming the issues related to handling large quantities of Heterogeneous Internet of Things (HIoT) data like distributed network and health monitoring by dispensing the applications which gets applied nearer to the network edge. In fog based network model, distributed fog server closeness to the terminal devices allows currently available data to be transmitted effectually. But the increasing size of fog enabled HIoT environments leads to the design of effective routing and path selection to accomplish minimum delay and power consumption. With this motivation, this paper presents Optimization based Data Aggregation and fault Tolerance with Energy Management (ODFTEM) technique for fog enabled HIoT. This idea is segmented into three categories; they are a quasi-oppositional chimp optimization based data aggregation for routing, fault tolerance amongst the fog nodes and a Markov-chain-based probabilistic model for energy management. Initially, data aggregation is employed to gather the data and accumulate it for removing repetitive data and conserving energy. The goal of data aggregation is to reduce quantity of data dissemination and extend lifetime. A quasi-oppositional chimp optimization based data aggregation in routing is performed to choose an optimal collection of routes and perform data aggregation and it follows two stages like relay node selection and data aggregation. Secondly, to boost the network performance the best cost path is chosen where it selects the best nearest neighbor so that the packet transmission time can get reduced. Finally to forecast the energy consumption a Markov chain based probabilistic approach is used and this method can provide a comfortable energy saving model to the fog network. A detailed experimental validation of the ODFTEM technique reported the better performance in terms of energy consumption, throughput and transmission delay of the devices in the Fog enabled HIoT environment which is compared with the baseline methods like IHCFC, DAIFC and ODIFC.

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Section: Related Workmentioning

confidence: 99%

Optimization based Data Aggregation and Fault Tolerance with Energy Management for Fog Enabled Heterogeneous IoT Environment

Nalayini,

jenifer,

Meena

2024

Preprint

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Delay aware resource allocation in ORAN through network optimization

Khalaf,

Ali,

Alhumaima

et al. 2024

IET Wireless Sensor Systems

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A multi variable resource allocation problem is investigated in network environments, specifically focusing on the consideration of quality of service in open radio access network. The main objective is to minimise the combined latency of various servers while complying with network limitations. The delay of each server is represented by a non‐linear function that has exponentially based. This characteristic inherently brings non‐convexity into the objective function. In contrast, the constraints comprise various linear combinations of network variables, including resource block allocations, power consumption, and number of virtual machines. The purpose of these constraints is to guarantee that the allocation of resources adheres to practical limitations and upholds fairness among servers. Nevertheless, the inclusion of a non‐convex objective function significantly adds complexity to the optimisation problem and non‐convex behaviour, requiring specialised algorithms and techniques to identify solutions. Subsequently, the Lagrange multiplier method has been used to solve this problem mathematically. Numerically, three algorithms have been utilised and compared to solve the problem, these are active‐set, interior point and sequential quadratic programming. Note that the total delay as an objective function is based on the total power consumption of the servers. Previous to optimising the total delay, a delay model is proposed and compared with two research works that are based on experimental and real time data. The proposed model shows data matching with the other works and permits for more adaptation/integration with any other works that uses different servers’ characteristics and network parameters.

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Latency minimization for multiuser computation offloading in fog-radio access networks

Cited by 2 publications

References 63 publications

Optimization based Data Aggregation and Fault Tolerance with Energy Management for Fog Enabled Heterogeneous IoT Environment

Optimization based Data Aggregation and Fault Tolerance with Energy Management for Fog Enabled Heterogeneous IoT Environment

Delay aware resource allocation in ORAN through network optimization

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