CAROL: Confidence-Aware Resilience Model for Edge Federations

TL;DR

CAROL is a confidence-aware resilience model for edge federations that uses generative neural networks to predict QoS and recover quickly from broker failures, improving efficiency and robustness in IoT applications.

Contribution

It introduces a novel confidence-aware neural network approach for resilient edge federation management, reducing overheads and improving QoS in dynamic IoT environments.

Findings

Reduces energy consumption by up to 16%

Decreases deadline violation rates by up to 17%

Lowers resilience overheads by up to 36%

Abstract

In recent years, the deployment of large-scale Internet of Things (IoT) applications has given rise to edge federations that seamlessly interconnect and leverage resources from multiple edge service providers. The requirement of supporting both latency-sensitive and compute-intensive IoT tasks necessitates service resilience, especially for the broker nodes in typical broker-worker deployment designs. Existing fault-tolerance or resilience schemes often lack robustness and generalization capability in non-stationary workload settings. This is typically due to the expensive periodic fine-tuning of models required to adapt them in dynamic scenarios. To address this, we present a confidence aware resilience model, CAROL, that utilizes a memory-efficient generative neural network to predict the Quality of Service (QoS) for a future state and a confidence score for each prediction. Thus, whenever a broker fails, we quickly recover the system by executing a local-search over the broker-worker topology space and optimize future QoS. The confidence score enables us to keep track of the prediction performance and run parsimonious neural network fine-tuning to avoid excessive overheads, further improving the QoS of the system. Experiments on a Raspberry-Pi based edge testbed with IoT benchmark applications show that CAROL outperforms state-of-the-art resilience schemes by reducing the energy consumption, deadline violation rates and resilience overheads by up to 16, 17 and 36 percent, respectively.