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Cited by 9IoT and Edge/Fog ComputingDistributed systems and fault toleranceEnergy Efficiency in Computing

CAROL: Confidence-Aware Resilience Model for Edge Federations

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Tuli, Shreshth, Casale, Giuliano, Jennings, Nicholas R.·2022·2022 52nd Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)
DOI 10.1109/dsn53405.2022.00016
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Abstract

In recent years, the deployment of large-scale Inter-net 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.

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  • DRAGON and CAROL emphasize detection accuracy and quality-of-service guarantees.

    “predictive frameworks such as DRAGON [36] and CAROL [37] emphasize detection accuracy and quality-of-service guarantees”

    From Digital Twins at the Edge: A High-Availability Framework for Resilient Data Processing in IoT Sensor Networks (2026) · cites this paper as [37]

    The Conclusions and Future Work section says CAROL predicts QoS scores and optimizes the topology after broker failure, supporting the quality-of-service focus. It does not claim QoS guarantees. The Performance Evaluation and Evaluation Metrics sections distinguish CAROL's resilience approach from fault-detection baselines and attribute the highest F1 score to StepGAN, not CAROL; they do not establish that CAROL emphasizes detection accuracy. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

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Research works printed but not linked: 18

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  3. Y. Feng, Z. Liu, J. Chen, H. Lv, J. Wang, and J. Yuan, "Make the rocket intelligent at iot edge: Stepwise gan for anomaly detection of lre with multi-source fusion," IEEE Internet of Things Journal, 2021.
  4. S. Tuli, S. Tuli, G. Casale, and N. R. Jennings, "Generative optimization networks for memory efficient data generation," Advances in Neural Information Processing Systems, Workshop on ML for Systems, 2021.
  5. M. Ashman, J. So, W. Tebbutt, V. Fortuin, M. Pearce, and R. E. Turner, "Sparse gaussian process variational autoencoders," arXiv preprint arXiv:2010.10177, 2020. · arXiv
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  10. S. Tuli, S. Tuli, R. Verma, and R. Tuli, "Modelling for prediction of the spread and severity of COVID-19 and its association with socioeconomic factors and virus types," MedRxiv, 2020.
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Authors
Tuli, S., Casale, G., & Jennings, N. R.
Affiliations
  • Imperial College London (GB)
  • Loughborough University (GB)
ORCID
Venue
2022 52nd Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)
Publisher
IEEE
Published
2022-06
Pages
28-40
ISSN
1530-0889 (ISSN-L)
DOI
10.1109/dsn53405.2022.00016
OpenAlex
W4221142885
Cited by
9
References
37
Citations by year
2026: 3 · 2024: 3 · 2023: 3
FWCI
1.45
Type
conference paper
Language
en
Open access
Licence
Locations
  • International Conference on Dependable Systems & Networks/Proceedings (publishedVersion)
  • arXiv (Cornell University) (submittedVersion, cc-by)
  • Spiral (Imperial College London) (submittedVersion)
  • arXiv (Cornell University) (cc-by)
Topics
  • Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
  • Physical Sciences › Computer Science › Computer Networks and Communications › Distributed systems and fault tolerance
  • Physical Sciences › Computer Science › Hardware and Architecture › Energy Efficiency in Computing
Keywords
service resilience, generative neural networks, quality of service prediction, neural network fine-tuning, Internet of Things, deadline violations, energy consumption

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