# CAROL: Confidence-Aware Resilience Model for Edge Federations

arXiv: https://arxiv.org/abs/2203.07140 · HTML: https://mlsanity.com/paper/2203.07140/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 58 references that are in the map.
- First posted on arXiv 14 Mar 2022.

## Builds on (most cited first, 18 of 18)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Confidence-Aware Learning for Deep Neural Networks](https://mlsanity.com/paper/2007.01458/) (2020)
- [Communication-Computation Trade-Off in Resource-Constrained Edge Inference](https://mlsanity.com/paper/2006.02166/) (2020)
- [Sparse Gaussian Process Variational Autoencoders](https://mlsanity.com/paper/2010.10177/) (2020)
- [COSCO: Container Orchestration using Co-Simulation and Gradient Based Optimization for Fog Computing Environments](https://mlsanity.com/paper/2104.14392/) (2021)
- [Dynamic Scheduling for Stochastic Edge-Cloud Computing Environments using A3C learning and Residual Recurrent Neural Networks](https://mlsanity.com/paper/2009.02186/) (2020)
- [FogBus: A Blockchain-based Lightweight Framework for Edge and Fog Computing](https://mlsanity.com/paper/1811.11978/) (2018)
- [DeFog: Fog Computing Benchmarks](https://mlsanity.com/paper/1907.10890/) (2019)
- [Edge AIBench: Towards Comprehensive End-to-end Edge Computing Benchmarking](https://mlsanity.com/paper/1908.01924/) (2019)
- [Generative Optimization Networks for Memory Efficient Data Generation](https://mlsanity.com/paper/2110.02912/) (2021)
- [GOSH: Task Scheduling Using Deep Surrogate Models in Fog Computing Environments](https://mlsanity.com/paper/2112.08916/) (2021)
- [Decentralized Edge-to-Cloud Load-balancing: Service Placement for the Internet of Things](https://mlsanity.com/paper/2005.00270/) (2020)
- [DYVERSE: DYnamic VERtical Scaling in Multi-tenant Edge Environments](https://mlsanity.com/paper/1810.04608/) (2018)
- [Sage: Leveraging ML to Diagnose Unpredictable Performance in Cloud Microservices](https://mlsanity.com/paper/2112.06263/) (2021)
- [A comparison of semi-deterministic and stochastic search techniques](https://mlsanity.com/paper/1605.05782/) (2016)

## Built on by (most cited first, 3 of 3)

- [Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Survey](https://mlsanity.com/paper/2505.01821/) (2025)
- [DRAGON: Decentralized Fault Tolerance in Edge Federations](https://mlsanity.com/paper/2208.07658/) (2022)
- [MetaNet: Automated Dynamic Selection of Scheduling Policies in Cloud Environments](https://mlsanity.com/paper/2205.10642/) (2022)

## Abstract (arXiv)

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.