Composite Resource Scheduling for Networked Control Systems

TL;DR

This paper presents a new model for composite resource scheduling in real-time networked control systems, addressing the joint scheduling of network and computing resources to ensure QoS, and proposes algorithms for special cases and a greedy approach for the general case.

Contribution

Introduces a novel composite resource scheduling model for NCSs, with optimal algorithms for special cases and a greedy strategy for the general case, validated by extensive experiments.

Findings

Optimal algorithms effectively solve special cases.

Greedy strategy improves scheduling in the general case.

Algorithms verified through extensive experiments.

Abstract

Real-time end-to-end task scheduling in networked control systems (NCSs) requires the joint consideration of both network and computing resources to guarantee the desired quality of service (QoS). This paper introduces a new model for composite resource scheduling (CRS) in real-time networked control systems, which considers a strict execution order of sensing, computing, and actuating segments based on the control loop of the target NCS. We prove that the general CRS problem is NP-hard and study two special cases of the CRS problem. The first case restricts the computing and actuating segments to have unit-size execution time while the second case assumes that both sensing and actuating segments have unit-size execution time. We propose an optimal algorithm to solve the first case by checking the intervals with 100% network resource utilization and modify the deadlines of the tasks within those intervals to prune the search. For the second case, we propose another optimal algorithm based on a novel backtracking strategy to check the time intervals with the network resource utilization larger than 100% and modify the timing parameters of tasks based on these intervals. For the general case, we design a greedy strategy to modify the timing parameters of both network segments and computing segments within the time intervals that have network and computing resource utilization larger than 100%, respectively. The correctness and effectiveness of the proposed algorithms are verified through extensive experiments.