eRACANN: Modular Neural Agents for Interpretable Cloud Resource Forecasting

Abstract

—As cloud computing becomes foundational to sectors like healthcare, finance, and artificial intelligence, accurate resource utilization forecasting has emerged as a critical challenge for ensuring efficiency, cost-effectiveness, and service reliability. This research introduces Runtime-Assembled Context-Specific Cooperative Artificial Neural Networks (RACANN), a novel modular neural framework designed to predict cloud resource utilization with improved interpretability and efficiency. Unlike monolithic deep learning models, RACANN dynamically assembles lightweight context-specific neural agents at runtime, enabling fine-grained temporal adaptability and significantly reducing computational overhead. We further propose eRACANN, an explainable extension that embeds a fuzzy logic-based linguistic layer, offering human-readable justifications for predictions. Experimental results across both structured and noisy real-world datasets demonstrate that while deep neural networks (DNNs) may achieve marginally lower error rates, eRACANN excels in modularity, interpretability, and contextual transparency which are critical properties for operational deployment in dynamic, mission-critical cloud environments. The RACANN framework offers a scalable path toward explainable, adaptive, and resource-efficient AI for cloud infrastructure management.

Department(s)

Computer Science

Document Type

Conference Proceeding

DOI

10.1109/JCC67032.2025.00014

Keywords

Artificial Neural Networks, Cloud, Machine Learning, Multi-Layer Perceptron, Resource Prediction

Publication Date

1-1-2025

Journal Title

Proceedings 2025 IEEE International Conference on Joint Cloud Computing Jcc 2025

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