車聯網 (Vehicle-to-Everything, V2X) 是5G 無線技術提供的服務之一。V2X 中的數據流量(Data Flow)需要通信和計算，而工作卸載(Task Offloading)預期為解決V2X計算的有效方法。目前，多數的卸載研究討論在演算法以及平均降低延遲之上，並無對設備可能故障進行較深入的討論及提出有效的容錯(Fault Tolerance)解決方法。因此，本碩士論文在保證車輛服務的服務品質(Quality of Service, QoS)的同時，亦考慮設備具潛在故障，以達到更周延的考量與卸載之設計。首先，本碩士論文參考[1]之二層卸載架構，亦即將路側設備(Road Side Unit, RSU) 和基地台(gNodeB, gNB) 的聯合卸載架構做適度擴充，加入鄰近車輛(Nearby Vehicles)，同時讓gNB由多個伺服器(Mutiple Servers)組成；此外，提出上述三種設備潛在故障出現時的容錯方法。最後，以最小化QoS違反機率(QoS violation probability)下，透過最佳化問題(Optimization Problem Formulation)形成找到最佳工作卸載路徑機率來最小化平均封包延遲等效能。為了優化gNB之多個伺服器效能，伺服器間的負載平衡(Load Balancing)設計也納入考量，工作延遲可近一步優化，而透過模擬以及驗證過後的分析結果，我們不僅呈現所提出之聯合卸載架構較[1]之架構可有效提升整體系統的QoS以及降低工作的封包平均延遲外，更能展現對潛在伺服器故障之容錯能力，而三種卸載容錯設計之系統動態(System Dynamics)也充分被探討，以推薦最佳之卸載容錯設計。

Vehicle-to-everything (V2X) is one of the services provided by the 5th generation (5G) mobile communication system. Data flows in V2X require communication and computing and task offloading is expected to be an effective approach to solve the V2X computing. Up to now, most offloading research merely discusses algorithm design and average delay reduction without conducting in-depth issues, e.g., possible device failures, thus proposing effective fault tolerance solutions. Therefore, this thesis considers the potential device failures to achieve a more thoughtful consideration on the offloading design while ensuring the quality of service (QoS) requested vehicles by vehicles. First of all, this thesis will modify the two-layer offloading architecture proposed by [1], i.e., the federated offloading architecture composed of road side unit (RSU) and gNodeB (gNB), to further incorporate the nearby vehicles to form the three-layer federated offloading architecture and allow the gNB to be supported by multiple servers. In addition, we shall propose the fault tolerance for the after mentioned three kinds of devices with potential failures. Lastly, the optimization problem is formulated to find the probability of each task offloading path by minimizing the QoS violation probability, to minimize the performance measure such as the average task delay. In order to optimize the performance of the multiple servers of gNB, a load balancing design among servers is further taken into consideration, reaching the optimized task delay. Via simulations and the validated analytical results, we not only demonstrate that our proposed federated offloading architecture outperforms the architecture proposed by [1] for effectively improving the QoS of the overall system and reducing the average task delay but also exhibit excellent capability of fault tolerance to potential server failures. Consequently, the system dynamics of the three fault-tolerance designs for offloading are fully examined to reveal the best fault-tolerant design among the three proposed solutions.

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