Resilient Cloud Control System: Realizing resilient cloud-based optimal control for cyber-physical systems

TL;DR

This paper presents a resilient cloud control architecture that adapts to cloud and network variability, enabling cyber-physical systems to maintain performance and survive disruptions through continuous adaptation and cloud switching.

Contribution

It introduces an architecture for resilient cloud-based control that dynamically adapts to QoS fluctuations and supports seamless cloud switching for cyber-physical systems.

Findings

Control system survives interruptions and noisy environments.

Adaptive control maintains performance despite resource variability.

Seamless cloud switching prevents control failure.

Abstract

The transformation to smart factories and the automation of mobile robotics is partly driven by a growing availability of ubiquitous cloud technologies. In cyber-physical systems, such as control systems, critical parts can be migrated to a cloud for offloading, enabling collaborative processes, improved performance, and life-cycle management. Despite the performance uncertainty in a cloud and the intermediate networks, presently, even cloud native function services are being investigated for supporting critical applications that are sensitive to time-varying execution and communication delays. In this paper, we introduce, implement, and empirically evaluate an architecture that successfully allows predictive controllers to take advantage of cloud native technology. Our solution relies on continuously adapting the control system to the present Quality of Service of the cloud and the intermediate network. As our results show, this allows a control system to survive interruptions, noisy neighbors, and time-variant resource availability. Without the proposed solution, the control system will fail due to resource constraints and insufficient response times. Further, we also show a system that can seamlessly switch between clouds and that multiple controllers using shared resources consequentially self-adapt so that no controller fails its objective.