Business-Semantic-Aware Joint Orchestration of Data, Computing Services, and Resources for Smart Grid Data Fabrics
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Abstract
Smart-grid operational analytics rely on heterogeneous data, analytical services, and distributed computing resources, but existing discovery and unified-access mechanisms do not ensure executable orchestration under operational constraints. This paper proposes BS-DCSO, a business-semantic-aware method for jointly selecting data services, computing services, and resource nodes in a smart-grid data-fabric environment. BS-DCSO integrates type- and permission-aware filtering, QoS-based top-k pruning, business-context scoring, complete-plan evaluation, feasibility-first lexicographic selection, and failure-triggered re-orchestration. Experiments on a synthetic benchmark covering five representative smart-grid scenarios and ten random seeds show a provisioning success rate of 0.4897 ± 0.1139, a feasible-instance success rate of 0.8320 ± 0.0562, and a constraint violation rate of 0.0809 ± 0.0213. Compared with ResourceFirstScheduling, BS-DCSO improves paired provisioning success by 0.1423 ± 0.0552, while outperforming five controlled baselines across the three main metrics. Re-orchestration recovers feasible replacements in 0.8282 ± 0.0398 of eligible single-service failures. Under the sequential fixed-catalogue timing protocol, the method requires 151.39–175.58 ms per request for 100–1000 requests, indicating a practical feasibility–computation trade-off under the tested synthetic setting rather than high-concurrency scalability.
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