A Self-stabilizing Control Plane for the Edge and Fog Ecosystems

TL;DR

This paper introduces a self-stabilizing, fault-tolerant control plane framework for fog computing ecosystems that guarantees rapid recovery from various failures, ensuring reliable and scalable fog services.

Contribution

It presents novel self-stabilizing algorithms for distributed control planes that recover from broad fault models within a constant number of communication rounds.

Findings

Guarantees fault recovery within a constant number of rounds

Ensures correct analytics computation during recovery

Overhead scales linearly with IoT load

Abstract

Fog Computing is now emerging as the dominating paradigm bridging the compute and connectivity gap between sensing devices (a.k.a. "things") and latency-sensitive services. However, as fog deployments scale by accumulating numerous devices interconnected over highly dynamic and volatile network fabrics, the need for self-configuration and self-healing in the presence of failures is more evident now than ever. Using the prevailing methodology of self-stabilization, we propose a fault-tolerant framework for distributed control planes that enables fog services to cope and recover from a very broad fault model. Specifically, our model considers network uncertainties, packet drops, node fail-stop failures, and violations of the assumptions according to which the system was designed to operate, such as an arbitrary corruption of the system state. Our self-stabilizing algorithms guarantee automatic recovery within a constant number of communication rounds without the need for external (human) intervention. To showcase the framework's effectiveness, the correctness proof of the proposed self-stabilizing algorithmic process is accompanied by a comprehensive evaluation featuring an open and reproducible testbed utilizing real-world data from the intelligent transportation domain. Results show that our framework ensures a fog ecosystem recovery from faults in constant time, analytics are computed correctly, while the overhead to the system's control plane scales linearly towards the IoT load.