On the Scheduling Policy for Multi-process WNCS under Edge Computing

Abstract

This paper considers a multi-process and multicontroller wireless networked control system (WNCS). There are N independent linear time-invariant processes in the system plant which represent different kinds of physical processes. By considering the edge computing, the controllers are played by edge server and cloud server. Each process is measured by a sensor, and the status updates is sent to controller to generate the control command. The link delay of cloud server is longer than that of edge server. The processing time of status update depends on the characteristic of servers and processes. By taking into account such conditions, we mainly investigate how to choose the destination of status updates to minimize the system's average Mean Square Error (MSE), edge server or cloud server? To address this issue, we formulate an infinite horizon average cost Markov Decision Process (MDP) problem and obtain the optimal scheduling policy. The monotonicity of the value function in MDP is characterized and then used to show the threshold structure properties of the optimal scheduling policy. To overcome the curse of dimensionality, we propose a low-complexity suboptimal policy by using additive separable structure of value function. Furthermore, the processing preemption mechanism is considered to handle the status updates more flexible, and the consistency property is proved. On this basis, a numerical example is provided. The simulation results illustrate that the controller selection is related to the timeliness of process and show the performance of the suboptimal policy. We also find that the optimal policy will become a static policy in which the destination of status update is fixed when the wireless channel is error free.

A Review Study on Energy Consumption in Cloud Computing

“…The study [68] investigates cost-efficient scheduling of containerized workloads in the cloud by considering resource costs and execution time. Another study [63] proposes a priority-aware scheduling algorithm for serverless functions in edge computing environments, ensuring timely execution of critical tasks. The paper [65] combines reinforcement learning and heuristic techniques for scheduling tasks and allocating resources in cloud environments, leading to enhanced performance.…”