Edge/fog computing, as a distributed computing paradigm, satisfies the low-latency requirements of ever-increasing number of IoT applications and has become the mainstream computing paradigm behind IoT applications. However, because large number of IoT applications require execution on the edge/fog resources, the servers may be overloaded. Hence, it may disrupt the edge/fog servers and also negatively affect IoT applications' response time. Moreover, many IoT applications are composed of dependent components incurring extra constraints for their execution. Besides, edge/fog computing environments and IoT applications are inherently dynamic and stochastic. Thus, efficient and adaptive scheduling of IoT applications in heterogeneous edge/fog computing environments is of paramount importance. However, limited computational resources on edge/fog servers imposes an extra burden for applying optimal but computationally demanding techniques. To overcome these challenges, we propose a Deep Reinforcement Learning-based IoT application Scheduling algorithm, called DRLIS to adaptively and efficiently optimize the response time of heterogeneous IoT applications and balance the load of the edge/fog servers. We implemented DRLIS as a practical scheduler in the FogBus2 function-as-a-service framework for creating an edge-fog-cloud integrated serverless computing environment. Results obtained from extensive experiments show that DRLIS significantly reduces the execution cost of IoT applications by up to 55%, 37%, and 50% in terms of load balancing, response time, and weighted cost, respectively, compared with metaheuristic algorithms and other reinforcement learning techniques.

翻译：边缘/雾计算作为一种分布式计算范式，满足了日益增长的物联网应用对低延迟的需求，已成为物联网应用的主流计算范式。然而，大量物联网应用需要在边缘/雾资源上执行，可能导致服务器过载。这种情况不仅会干扰边缘/雾服务器的稳定运行，还会对物联网应用的响应时间产生负面影响。此外，许多物联网应用由相互依赖的组件构成，这为其执行带来了额外的约束条件。同时，边缘/雾计算环境和物联网应用本质上具有动态性和随机性。因此，在异构边缘/雾计算环境中对物联网应用进行高效自适应的调度至关重要。但边缘/雾服务器有限的计算资源给应用最优但计算密集型技术带来了额外负担。为应对这些挑战，我们提出了一种基于深度强化学习的物联网应用调度算法DRLIS，可自适应且高效地优化异构物联网应用的响应时间并平衡边缘/雾服务器的负载。我们在FogBus2函数即服务框架中将DRLIS实现为实用调度器，用于构建边缘-雾-云集成的无服务器计算环境。大量实验结果表明，与元启发式算法及其他强化学习技术相比，DRLIS在负载均衡、响应时间和加权成本方面分别使物联网应用的执行成本降低了55%、37%和50%。