With the rapid advancement of devices requiring intensive computation, such as Internet of Things (IoT) devices, smart sensors, and wearable technology, the computational demands on individual platforms with limited resources have escalated, necessitating the offloading of the generated tasks by the devices to edge. These tasks are often real-time with strict response time requirements. Among these devices, autonomous vehicles present unique challenges due to their critical need for timely and accurate processing to ensure passenger safety. Selecting suitable servers in a heterogeneous mobile edge computing (MEC) architecture is vital to optimizing real-time task processing rates for such applications. To address this, we present an algorithmic solution to improve the allocation of heterogeneous servers to real-time tasks, aiming to maximize the number of processed tasks. By analyzing task and server characteristics in the MEC architecture, we develop the suitability-based adaptive resource selection (SARS) algorithm, which evaluates server suitability based on factors like time constraints and server capabilities. Additionally, we introduce the proactive on-demand resource allocation (PORA) algorithm, which strategically reserves computational resources to ensure availability for critical real-time tasks. We compare the proposed algorithms with several classical and state-of-the-art algorithms. Computational results demonstrate that our approach outperforms existing algorithms, processes more tasks, and effectively prioritizes urgent tasks, particularly in autonomous driving applications.

翻译：随着物联网设备、智能传感器和可穿戴技术等需要密集计算的设备快速发展，资源有限的独立平台面临日益增长的计算需求，这促使设备将生成的任务卸载至边缘处理。此类任务通常具有实时性，对响应时间有严格要求。在这些设备中，自动驾驶车辆因对及时精准处理的迫切需求而面临独特挑战，这直接关系到乘客安全。在异构移动边缘计算架构中选择合适的服务器，对于优化此类应用的实时任务处理速率至关重要。为此，我们提出一种算法解决方案，旨在改进异构服务器对实时任务的分配机制，以最大化可处理任务数量。通过分析MEC架构中的任务与服务器特性，我们开发了基于适应性的自适应资源选择算法，该算法依据时间约束与服务器能力等因素评估服务器适配度。此外，我们提出前瞻性按需资源分配算法，通过策略性预留计算资源确保关键实时任务的可用性。我们将所提算法与多种经典及前沿算法进行比较。计算结果表明，我们的方法在自动驾驶等应用中优于现有算法，能处理更多任务并有效优先处理紧急任务。