Vehicular Edge Computing (VEC) is considered a major enabler for multi-service vehicular 6G scenarios. However, limited computation, communication, and storage resources of terrestrial edge servers are becoming a bottleneck and hindering the performance of VEC-enabled Vehicular Networks (VNs). Aerial platforms are considered a viable solution allowing for extended coverage and expanding available resources. However, in such a dynamic scenario, it is important to perform a proper service placement based on the users' demands. Furthermore, with limited computing and communication resources, proper user-server assignments and offloading strategies need to be adopted. Considering their different time scales, a multi-time-scale optimization process is proposed here to address the joint service placement, network selection, and computation offloading problem effectively. With this scope in mind, we propose a multi-time-scale Markov Decision Process (MDP) based Reinforcement Learning (RL) to solve this problem and improve the latency and energy performance of VEC-enabled VNs. Given the complex nature of the joint optimization process, an advanced deep Q-learning method is considered. Comparison with various benchmark methods shows an overall improvement in latency and energy performance in different VN scenarios.
Multi-Time-Scale Markov Decision Process for Joint Service Placement, Network Selection, and Computation Offloading in Aerial IoV Scenarios / Shinde, Swapnil Sadashiv; Tarchi, Daniele. - In: IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING. - ISSN 2327-4697. - ELETTRONICO. - 11:(2024), pp. 5364-5379. [10.1109/tnse.2024.3445890]
Multi-Time-Scale Markov Decision Process for Joint Service Placement, Network Selection, and Computation Offloading in Aerial IoV Scenarios
Tarchi, Daniele
2024
Abstract
Vehicular Edge Computing (VEC) is considered a major enabler for multi-service vehicular 6G scenarios. However, limited computation, communication, and storage resources of terrestrial edge servers are becoming a bottleneck and hindering the performance of VEC-enabled Vehicular Networks (VNs). Aerial platforms are considered a viable solution allowing for extended coverage and expanding available resources. However, in such a dynamic scenario, it is important to perform a proper service placement based on the users' demands. Furthermore, with limited computing and communication resources, proper user-server assignments and offloading strategies need to be adopted. Considering their different time scales, a multi-time-scale optimization process is proposed here to address the joint service placement, network selection, and computation offloading problem effectively. With this scope in mind, we propose a multi-time-scale Markov Decision Process (MDP) based Reinforcement Learning (RL) to solve this problem and improve the latency and energy performance of VEC-enabled VNs. Given the complex nature of the joint optimization process, an advanced deep Q-learning method is considered. Comparison with various benchmark methods shows an overall improvement in latency and energy performance in different VN scenarios.File | Dimensione | Formato | |
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Multi-Time-Scale_Markov_Decision_Process_for_Joint_Service_Placement_Network_Selection_and_Computation_Offloading_in_Aerial_IoV_Scenarios.pdf
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