Real-Time QoS Optimization for Vehicular Edge Computing With Off-Grid Roadside Units

Ku, Yu-Jen; Chiang, Po-Han; Dey, Sujit

doi:10.1109/tvt.2020.3007640

Cited by 21 publications

(6 citation statements)

References 40 publications

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“…Moreover, in [31], a system of base stations along roads is suggested to provide real-time responses to self-driving cars, mitigating the dire consequences of delays. However, reliance on solar power poses challenges for automotive applications.…”

Section: B Congestion Controlmentioning

confidence: 99%

Optimizing the Over and Underutilization of Network Resources During Peak and Off-Peak Hours

Badshah,

Daud,

Khan

et al. 2024

IEEE Access

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In 2022, the digital population reached 5.4 billion, leading to network congestion, primarily attributed to the widespread use of social media, robotic programs, and prime-time television during peak hours. This congestion posed a significant challenge, as sensitive services struggled to operate effectively during peak hours, while network resources remained largely underutilized during off-peak hours. To tackle this issue, we propose the implementation of Regional Servers. These servers are designed to initially store less critical data during peak hours and subsequently transfer it to the cloud during off-peak hours, effectively optimizing network resources. The results demonstrate that the deployment of regional servers proved highly efficient in mitigating delays and reducing jitters that users typically experience during peak hours, successfully addressing the challenges posed by network congestion.

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Section: B Congestion Controlmentioning

confidence: 99%

Optimizing the Over and Underutilization of Network Resources During Peak and Off-Peak Hours

Badshah,

Daud,

Khan

et al. 2024

IEEE Access

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“…As the traffic generated by the AI-enabled VEC systems exhibits a high degree of burstiness due to the spatial and temporal interaction feature and the complex vehicle environment, MMPP is exploited in this study to model the traffic arrivals of VEC systems. In addition, due to the features of dynamic traffic arrivals, unstable wireless transmission, and varying resource availability, most of the existing offloading strategies target to optimise the QoS metrics from the perspective of statistical average, such as the average offloading time [5] [8] [14], average virtual machine migration costs [27], average queueing time [15] [16], average power consumption [13], average throughput [4] [26], and so on. In line with the existing work on task offloading, this paper aims to develop a new analytical model capable of quantitatively studying the average performance metrics of VEC systems with bursty task arrivals.…”

Section: Related Workmentioning

confidence: 99%

Performance Modelling and Quantitative Analysis of Vehicular Edge Computing With Bursty Task Arrivals

Miao

Min

Zhang

et al. 2023

IEEE Trans. on Mobile Comput.

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The quantitative performance analysis plays a critical role in assessing the capability of Vehicular Edge Computing (VEC) systems to meet the requirements of vehicular applications. However, developing accurate analytical models for VEC systems is extremely challenging due to the unique features of intelligent vehicular applications. Specifically, recent work revealed that the tasks generated by intelligent vehicular applications exhibit a high degree of burstiness, rendering the existing models that were designed based on the assumption of the non-bursty Poisson process unsuitable for VEC systems. To fill this gap, we developed an original analytical model to investigate the performance of VEC systems with bursty task arrivals. To facilitate vehicle cooperation, a new priority-based resource allocation scheme is exploited to schedule the tasks of vehicular applications, which are modelled by a Markov Modulated Poisson Process (MMPP). Next, a multi-state Markov chain is established to investigate the impact of load sharing strategy on the performance of VEC systems. Then, the end-to-end transmission latency is derived based on the proposed model. Comprehensive experiments are conducted to validate the accuracy of this analytical model under various system configurations. Furthermore, the developed model is used as a cost-effective tool to investigate the performance bottleneck of VEC systems.

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“…With the policy, the battery of an RSU is extended and the safety of environment is promoted while the quality of service (QoS) levels is met. Aiming at the problem of power deficiency in solar-powered roadside units (SRSUs), the challenge of QoS loss is addressed in [21], in which a two-phase approach is proposed. With the purpose of energy consumption and time delay guarantee, a distributed packet scheduling optimization strategy is proposed in [22] for the renewable energypowered RSUs.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning‐Based Service Scheduling Mechanism for GreenRSUs in the IoVs

Wang

Seo

et al. 2021

Wireless Communications and Mobile Computing

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Green roadside units (RSUs), also called renewable energy-powered RSUs, are utilized recently rather than the traditional electric-powered RSUs with high power consumption and the large infrastructure deployment cost in the Internet of vehicles (IoVs). However, the power of the green RSUs is limited and unstable, which is affected by the battery size and charging environment. Therefore, a big challenge to deploy green RSUs in the IoVs is to schedule their service process properly, in order to extend the service efficiency of RSUs. In this paper, a deep learning-based communication scheduling mechanism is proposed regarding the service scheduling problem. In particular, a three-part scheduling algorithm consisting of RSU clustering, deep learning-based traffic prediction, and a vehicle access scheduling algorithm is presented to maximize the service number of vehicles and minimize the energy cost. An extensive simulation is done, and the simulation results indicate that our algorithm can serve more vehicles with less energy consumption compared with other scheduling mechanisms under different scenarios.

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Real-Time QoS Optimization for Vehicular Edge Computing With Off-Grid Roadside Units

Cited by 21 publications

References 40 publications

Optimizing the Over and Underutilization of Network Resources During Peak and Off-Peak Hours

Optimizing the Over and Underutilization of Network Resources During Peak and Off-Peak Hours

Performance Modelling and Quantitative Analysis of Vehicular Edge Computing With Bursty Task Arrivals

Deep Learning‐Based Service Scheduling Mechanism for GreenRSUs in the IoVs

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