Hao Yixue, Chen Min, Gharavi Hamid, Zhang Yin, Hwang Kai
School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430 074, China.
School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430 074, China, and also with the Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen 518 172, China.
IEEE Trans Industr Inform. 2021 Aug;17(8). doi: 10.1109/tii.2020.3041713.
Future industrial cyber-physical system (CPS) devices are expected to request a large amount of delay-sensitive services that need to be processed at the edge of a network. Due to limited resources, service placement at the edge of the cloud has attracted significant attention. Although there are many methods of design schemes, the service placement problem in industrial CPS has not been well studied. Furthermore, none of existing schemes can optimize service placement, workload scheduling, and resource allocation under uncertain service demands. To address these issues, we first formulate a joint optimization problem of service placement, workload scheduling, and resource allocation in order to minimize service response delay. We then propose an improved deep Q-network (DQN)-based service placement algorithm. The proposed algorithm can achieve an optimal resource allocation by means of convex optimization where the service placement and workload scheduling decisions are assisted by means of DQN technology. The experimental results verify that the proposed algorithm, compared with existing algorithms, can reduce the average service response time by 8-10%.
未来的工业信息物理系统（CPS）设备预计将请求大量对延迟敏感的服务，这些服务需要在网络边缘进行处理。由于资源有限，在云边缘进行服务部署引起了广泛关注。尽管有许多设计方案的方法，但工业CPS中的服务部署问题尚未得到充分研究。此外，现有的方案都无法在不确定的服务需求下优化服务部署、工作负载调度和资源分配。为了解决这些问题，我们首先提出一个服务部署、工作负载调度和资源分配的联合优化问题，以最小化服务响应延迟。然后，我们提出一种基于深度Q网络（DQN）改进的服务部署算法。该算法通过凸优化实现最优资源分配，其中服务部署和工作负载调度决策借助DQN技术辅助。实验结果验证，与现有算法相比，该算法可将平均服务响应时间降低8-10%。
