Optimal Pricing for Job Offloading in the MEC System With Two Priority Classes

Li, Lingxiang; Siew, Marie; Chen, Zhi; Quek, Tony Q. S.

doi:10.1109/tvt.2021.3090935

Cited by 6 publications

(1 citation statement)

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“…Furthermore, vehicles are hesitant to report profile functions as they contain sensitive information such as battery states [3]. Given that, various vehicles place varying negative values on delay, as some are more delay-sensitive than others [4]. Thus, Mobile Network Operators (MNOs) find themselves intertwined in this profit-generating sector with competition for cached content in shared storage controlled by numerous MNOs, as well as pricing concerns [5].…”

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confidence: 99%

Intelligent Pricing Model for Task Offloading in Unmanned Aerial Vehicle Mounted Mobile Edge Computing for Vehicular Network

Baktayan

Al-Baltah

Ghani

2022

JCOMSS

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In the fifth-generation (5G) cellular network, the Mobile Network Operator (MNO), and the Mobile Edge Computing (MEC) platform will play an important role in providing services to an increasing number of vehicles. Due to vehicle mobility and the rise of computation-intensive and delaysensitive vehicular applications, it is challenging to achieve the rigorous latency and reliability requirements of vehicular communication. The MNO, with the MEC server mounted on an unmanned aerial vehicle (UAV), should make a profit by providing its computing services and capabilities to moving vehicles. This paper proposes the use of dynamic pricing for computation offloading in UAV-MEC for vehicles. The novelty of this paper is in how the price influences offloading demand and decides how to reduce network costs (delay and energy) while maximizing UAV operator revenue, but not the offloading benefits with the mobility of vehicles and UAV. The optimization problem is formulated as a Markov Decision Process (MDP). The MDP can be solved by the Deep Reinforcement Learning (DRL) algorithm, especially the Deep Deterministic Policy Gradient (DDPG). Extensive simulation results demonstrate that the proposed pricing model outperforms greedy by 26% and random by 51% in terms of delay. In terms of system utility, the proposed pricing model outperforms greedy only by 17%. In terms of server congestion, the proposed pricing model outperforms random by 19% and is almost the same as greedy.

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confidence: 99%