An Adaptive and Dynamic Allocation of Delay-sensitive Vehicular Services in Federated Cloud

Najm, Moustafa; Patra, Moumita; Tamarapalli, Venkatesh

doi:10.1109/comsnets51098.2021.9352906

Cited by 6 publications

(2 citation statements)

References 14 publications

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“…For example, Najm et al [21] propose a delay-aware resource allocation method that considers an adaptive delay warning threshold for various users. Xu and Li [22] adopt a general fairness criterion based on Nash bargaining solutions, and present a general optimization framework that models the [23] present a method to jointly optimize the cost and the performance of delivering traffic from an online service provider (OSP) network to its users.…”

Section: Related Workmentioning

confidence: 99%

Joint Optimization for Bandwidth Utilization and Delay Based on Particle Swarm Optimization

Liu¹,

et al. 2021

IEEE Access

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One of the most significant problems in large-scale applications is how to allocate requests from end-users to datacenters. Most existing studies focus only on high bandwidth utilization of datacenters or low request delay of end-users, lacking the consideration of both at the same time. To achieve the joint optimization for bandwidth utilization of datacenter providers and delay of end-users, this paper proposes a novel Particle Swarm Optimization (PSO) based request allocation algorithm, based on the construction of a mathematical model for joint optimization. The algorithm adapts the coding scheme and redefines the operator to make PSO suitable for discrete optimization problems and use fitness function to get a global optimal solution. Through extensive simulations, it is demonstrated that the PSO based request allocation algorithm proposed in this study can improve the bandwidth utilization and keep the average request delay within acceptable limits or even better.

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Section: Related Workmentioning

confidence: 99%

Joint Optimization for Bandwidth Utilization and Delay Based on Particle Swarm Optimization

Liu¹,

et al. 2021

IEEE Access

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“…Terefore, vehicles that have completed training face both low computational efciency and poor training accuracy. For example, IoV is more sensitive to data transmission delays during model training [25][26][27]. Based on the heterogeneity of vehicles in the IoV scenario, computational and communication capabilities are diferent.…”

Section: Introductionmentioning

confidence: 99%

Towards Efficient Federated Learning Using Agile Aggregation in Internet of Vehicles

He,

Hu,

Wang

et al. 2023

Security and Communication Networks

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Federated learning is an enabling technology for the services in Internet of vehicles because it can effectively alleviate privacy issues in data circulation and diversified intelligent applications. However, existing federated learning methods still confront the problem of low computational efficiency when applied to the scenario of high-dynamic vehicles. In order to address this problem, we present a multistep federated learning architecture including task release, vehicle model training, identity authentication, quality assessment, and weighted aggregation processes, where a novel agile aggregation model is established. Based on the agile aggregation model, an efficient federated learning (EFL) algorithm is proposed, which is specifically applied to the scenario of high-dynamic vehicles during the training model. We theoretically analyze its advantages over the existing traditional federated learning algorithms in terms of efficiency, training accuracy, and security. Simulations are conducted using the MNIST dataset to evaluate the performance of the proposed EFL algorithm. The results show that EFL improves the efficiency of the federated training process with effective accuracy and privacy preservation.

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Mobility-Aware Proactive QoS Monitoring for Mobile Edge Computing

Wei

Zhang

Dong

et al. 2022

Service-Oriented Computing

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An Adaptive and Dynamic Allocation of Delay-sensitive Vehicular Services in Federated Cloud

Cited by 6 publications

References 14 publications

Joint Optimization for Bandwidth Utilization and Delay Based on Particle Swarm Optimization

Joint Optimization for Bandwidth Utilization and Delay Based on Particle Swarm Optimization

Towards Efficient Federated Learning Using Agile Aggregation in Internet of Vehicles

Mobility-Aware Proactive QoS Monitoring for Mobile Edge Computing

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