Virtualized Control over Fog: Interplay Between Reliability and Latency

TL;DR

This paper develops an analytical framework to optimize the placement of virtual controllers in fog computing environments, balancing reliability and latency for real-time IoT control systems, with a focus on low-latency applications like drone tracking.

Contribution

It introduces a novel model that accounts for both reliability and latency in fog controller placement, providing insights into their trade-offs for real-time control.

Findings

Latency has a greater impact than reliability on control performance.

Optimal control policies depend heavily on latency considerations.

Simulation with drone models confirms the importance of minimizing latency for autonomous vehicle control.

Abstract

This paper introduces an analytical framework to investigate optimal design choices for the placement of virtual controllers along the cloud-to-things continuum. The main application scenarios include low-latency cyber-physical systems in which real-time control actions are required in response to the changes in states of an IoT node. In such cases, deploying controller software on a cloud server is often not tolerable due to delay from the network edge to the cloud. Hence, it is desirable to trade reliability with latency by moving controller logic closer to the network edge. Modeling the IoT node as a dynamical system that evolves linearly in time with quadratic penalty for state deviations, recursive expressions for the optimum control policy and the resulting minimum cost value are obtained by taking virtual fog controller reliability and response time latency into account. Our results indicate that latency is more critical than reliability in provisoning virtualized control services over fog endpoints, as it determines the swiftness of the fog control system as well as the timeliness of state measurements. Based on a realistic drone trajectory tracking model, an extensive simulation study is also performed to illustrate the influence of reliability and latency on the control of autonomous vehicles over fog.