NeSy-Edge: Neuro-Symbolic Trustworthy Self-Healing in the Computing Continuum
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866910062851653632 |
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| author | Ye, Peihan Lapkovskis, Alfreds Saleh, Alaa Zhang, Qiyang Donta, Praveen Kumar |
| author_facet | Ye, Peihan Lapkovskis, Alfreds Saleh, Alaa Zhang, Qiyang Donta, Praveen Kumar |
| contents | The computational demands of modern AI services are increasingly shifting execution beyond centralized clouds toward a computing continuum spanning edge and end devices. However, the scale, heterogeneity, and cross-layer dependencies of these environments make resilience difficult to maintain. Existing fault-management methods are often too static, fragmented, or heavy to support timely self-healing, especially under noisy logs and edge resource constraints. To address these limitations, this paper presents NeSy-Edge, a neuro-symbolic framework for trustworthy self-healing in the computing continuum. The framework follows an edge-first design, where a resource-constrained edge node performs local perception and reasoning, while a cloud model is invoked only at the final diagnosis stage. Specifically, NeSy-Edge converts raw runtime logs into structured event representations, builds a prior-constrained sparse symbolic causal graph, and integrates causal evidence with historical troubleshooting knowledge for root-cause analysis and recovery recommendation. We evaluate our work on representative Loghub datasets under multiple levels of semantic noise, considering parsing quality, causal reasoning, end-to-end diagnosis, and edge-side resource usage. The results show that NeSy-Edge remains robust even at the highest noise level, achieving up to 75% root-cause analysis accuracy and 65% end-to-end accuracy while operating within about 1500 MB of local memory. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2603_21145 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | NeSy-Edge: Neuro-Symbolic Trustworthy Self-Healing in the Computing Continuum Ye, Peihan Lapkovskis, Alfreds Saleh, Alaa Zhang, Qiyang Donta, Praveen Kumar Distributed, Parallel, and Cluster Computing Artificial Intelligence Machine Learning Symbolic Computation The computational demands of modern AI services are increasingly shifting execution beyond centralized clouds toward a computing continuum spanning edge and end devices. However, the scale, heterogeneity, and cross-layer dependencies of these environments make resilience difficult to maintain. Existing fault-management methods are often too static, fragmented, or heavy to support timely self-healing, especially under noisy logs and edge resource constraints. To address these limitations, this paper presents NeSy-Edge, a neuro-symbolic framework for trustworthy self-healing in the computing continuum. The framework follows an edge-first design, where a resource-constrained edge node performs local perception and reasoning, while a cloud model is invoked only at the final diagnosis stage. Specifically, NeSy-Edge converts raw runtime logs into structured event representations, builds a prior-constrained sparse symbolic causal graph, and integrates causal evidence with historical troubleshooting knowledge for root-cause analysis and recovery recommendation. We evaluate our work on representative Loghub datasets under multiple levels of semantic noise, considering parsing quality, causal reasoning, end-to-end diagnosis, and edge-side resource usage. The results show that NeSy-Edge remains robust even at the highest noise level, achieving up to 75% root-cause analysis accuracy and 65% end-to-end accuracy while operating within about 1500 MB of local memory. |
| title | NeSy-Edge: Neuro-Symbolic Trustworthy Self-Healing in the Computing Continuum |
| topic | Distributed, Parallel, and Cluster Computing Artificial Intelligence Machine Learning Symbolic Computation |
| url | https://arxiv.org/abs/2603.21145 |