Edge computing enables Mobile Autonomous Systems (MASs) to execute continuous streams of heavy-duty mission-critical processing tasks, such as real-time obstacle detection and navigation. However, in practical applications, erratic patterns in channel quality, network load, and edge server load can interrupt the task flow execution, which necessarily leads to severe disruption of the system's key operations. Existing work has mostly tackled the problem with reactive approaches, which cannot guarantee task-level reliability. Conversely, in this paper we focus on learning-based predictive edge computing to achieve self-resilient task offloading. By conducting a preliminary experimental evaluation, we show that there is no dominant feature that can predict the edge-MAS system reliability, which calls for an ensemble and selection of weaker features. To tackle the complexity of the problem, we propose SeReMAS, a data-driven optimization framework. We first mathematically formulate a Redundant Task Offloading Problem (RTOP), where a MAS may connect to multiple edge servers for redundancy, and needs to select which server(s) to transmit its computing tasks in order to maximize the probability of task execution while minimizing channel and edge resource utilization. We then create a predictor based on Deep Reinforcement Learning (DRL), which produces the optimum task assignment based on application-, network- and telemetry-based features. We prototype SeReMAS on a testbed composed by a drone, mounting a PixHawk flight controller, a Jetson Nano board, and three 802.11n WiFi interfaces. We extensively evaluate SeReMAS by considering an application where one drone offloads high-resolution images for real-time analysis to three edge servers on the ground. Experimental results show that SeReMAS improves task execution probability by $17\%$ with respect to existing reactive-based approaches.
翻译:电磁计算使移动自动系统(MAS)能够执行连续流的重型任务关键处理任务,例如实时障碍检测和导航。然而,在实际应用中,频道质量、网络负荷和边端服务器负荷的不稳定模式会干扰任务流执行,这必然导致系统关键操作的严重中断。现有工作主要通过反应方法解决了问题,无法保证任务的可靠性。相反,在本文件中,我们侧重于基于学习的预测边缘计算,以实现自我恢复任务。通过进行初步实验评估,我们表明没有任何主要功能可以预测边缘-MAS系统的可靠性,这需要集合和选择较弱功能。为了应对问题的复杂性,我们建议SeREMAS,一个数据驱动的优化框架。我们首先数学地设计一个重置任务卸任务问题(RTOP),在这个问题上,MAS可能与基于多个边缘的服务器连接,用于冗余任务,并且需要选择哪个服务器来传输其计算任务,以便尽可能增加任务执行速度的概率,同时尽量减少频道和边端服务器的功能,我们用SeREMAS-RIMA 进行一个基于不断的智能任务变压的智能任务分析。