AI-Driven BERT Architectures in Smart Connect Ecosystems: A Comparative Study for Sustainable IT Operations and Data Governance
DOI:
https://doi.org/10.15662/IJARCST.2024.0705005Keywords:
BERT, Deep Neural Architectures, Data Governance, Smart Connect, Sustainable IT Operations, BiLSTM, CNN, Transformer Variants, Inference Latency, Energy EfficiencyAbstract
The integration of Smart Connect ecosystems—interconnected platforms of IoT devices, cloud/edge computing, and data pipelines—has intensified the need for robust data governance and sustainable IT operations. In this paper, we undertake a comparative study of several deep neural architectures enhanced with BERT (Bidirectional Encoder Representations from Transformers) for tasks relevant to data governance and sustainability in IT operations, such as anomaly detection, policy compliance monitoring, resource usage forecasting, and logs/text‐based reasoning. The architectures evaluated include BERT alone, BERT + BiLSTM, BERT + CNN, BERT + Transformer variants, and hybrid models combining attention and recurrent/convolutional modules. We explore how these architectures perform under multiple criteria: classification/regression accuracy, inference latency, energy consumption, model size, adaptability to concept drift, and interpretability. Experimental evaluation is performed on datasets derived from IT operations logs, compliance policy documents, and resource usage telemetry (both real and semi‑synthetic). Our findings show that hybrid models (e.g. BERT + BiLSTM or BERT + lightweight transformer heads) can achieve significant improvements in accuracy (up to ~5‑10%) over BERT alone for tasks involving sequential or temporal dependencies. However, these gains come at costs in model size, training/inference time, and energy consumption. Pure CNN atop BERT offers lower latency and smaller overhead but sometimes lags in capturing long‑range dependencies. We also discuss how data governance constraints (privacy, auditability) interact with architecture choice. The contributions of this paper are (i) an empirical comparison across a wide set of architectures in the Smart Connect / sustainable IT domain; (ii) quantification of trade‑offs in accuracy vs sustainability / cost; (iii) recommendations for deployment in real‑world systems with constraints. Implications include more informed architecture choices for enterprise systems seeking sustainable, compliant, and high‑performing AI components.
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