Collaborative cloud–edge computing has been systematically developed to balance the efficiency and cost of computing tasks for many emerging technologies. To improve the overall performance of cloud–edge system, existing works have made progress in task scheduling by dynamically distributing the tasks with different latency thresholds to edge and cloud nodes. However, the relationship of multi-resource queueing among different tasks within a node is not well studied, which leaves the merit of optimizing the multi-resource queueing unexplored. To fill this gap and improve the efficiency of cloud–edge system, we propose DeepMIC, a deep reinforcement learning (DRL)-based multi-resource interleaving scheme for task scheduling in cloud–edge system. First, we formulate a multi-resource queueing model aiming at minimizing the weighted-sum delay of the pending tasks. The proposed model jointly considers the requests for computation, caching, and forwarding resources within a node based on the network information collected through Software-Defined Networking (SDN) and the management framework of Mobile Edge Computing (MEC). Then, we customize a DRL algorithm to ensure a timely solution of the model, which caters to the high throughput of tasks. Finally, we demonstrate that through the flexible scheduling of the tasks, DeepMIC reduces the average task response time and achieves better resource utilization.

中文翻译：

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基于深度强化学习的云边系统多资源交错任务调度


云边协同计算已经被系统地开发出来，以平衡许多新兴技术的计算任务的效率和成本。为了提高云边系统的整体性能，现有工作通过将具有不同延迟阈值的任务动态分配到边缘和云节点，在任务调度方面取得了进展。然而，节点内不同任务之间的多资源排队关系还没有得到很好的研究，这使得优化多资源排队的优点没有被探索。为了填补这一空白并提高云边系统的效率，我们提出了 DeepMIC，一种基于深度强化学习（DRL）的多资源交错方案，用于云边系统中的任务调度。首先，我们制定了一个多资源排队模型，旨在最小化待处理任务的加权和延迟。该模型基于通过软件定义网络（SDN）收集的网络信息和移动边缘计算（MEC）的管理框架，共同考虑节点内对计算、缓存和转发资源的请求。然后，我们定制了DRL算法来保证模型的及时求解，迎合了任务的高吞吐量。最后，我们证明通过任务的灵活调度，DeepMIC降低了平均任务响应时间并实现了更好的资源利用率。