面向绿色油气行业的无服务器边缘计算
Serverless Edge Computing for Green Oil and Gas Industry
摘要：
自动驾驶汽车的开发需要敏捷可靠的服务来管理危险的道路状况。车联网是为自动驾驶汽车提供高质量服务的媒介。车辆网络中的大多数服务请求是延迟不容忍的（例如，危险警报、车道变换警报）并需要立即维修。因此，车辆网络，特别是车辆到基础设施（V2I）系统必须为自动驾驶车辆提供一致的实时响应。在高峰时段或灾难期间，当大量请求到达基站时，V2I系统很难保持其性能，这可能导致危险的后果。因此，本研究的目标是开发一个V2I系统，对不确定的请求到达是强大的。为了实现这一目标，我们建议动态分配基站之间的服务请求。我们开发了一种不确定性感知的资源分配方法的联合环境，分配到达的请求到基站，使按时完成它的可能性最大化。我们在各种工作负载条件和超额订阅水平下评估系统。仿真结果表明，边缘联邦可以提高V2I系统的鲁棒性，将整体服务错过率降低高达45%。
英文摘要：
Development of autonomous and self-driving vehicles requires agile and reliable services to manage hazardous road situations. Vehicular Network is the medium that can provide high-quality services for self-driving vehicles. The majority of service requests in Vehicular Networks are delay intolerant (e.g., hazard alerts, lane change warning) and require immediate service. Therefore, Vehicular Networks, and particularly, Vehicle-to-Infrastructure (V2I) systems must provide a consistent real-time response to autonomous vehicles. During peak hours or disasters, when a surge of requests arrives at a Base Station, it is challenging for the V2I system to maintain its performance, which can lead to hazardous consequences. Hence, the goal of this research is to develop a V2I system that is robust against uncertain request arrivals. To achieve this goal, we propose to dynamically allocate service requests among Base Stations. We develop an uncertainty-aware resource allocation method for the federated environment that assigns arriving requests to a Base Station so that the likelihood of completing it on-time is maximized. We evaluate the system under various workload conditions and oversubscription levels. Simulation results show that edge federation can improve robustness of the V2I system by reducing the overall service miss rate by up to 45%.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
