Uncertainty-Aware Predictive Modeling for Real-Time Decision Support in Distributed Computing Environments

  • Authors

    • Sri Veera Research Scholar, India. Author

    DOI:

    https://doi.org/10.67228/30715636/IJETMR-V9I3P103

    Published 07-07-2026

  • Distributed Computing, Uncertainty Quantification, Probabilistic Forecasting, Real-Time Decision Support, Workload Prediction, Resource Allocation, Adaptive Scheduling, Prediction Intervals, Service-Level Objectives, Edge-Cloud Computing

    Issue

    Vol. 9 No. 3 (2026)

    Section

    Articles

  • Abstract

    Distributed computing environments are used for a variety of applications such as cloud computing, the edge, the Internet of Things (IoT), industrial automation, healthcare, and intelligent transportation. Conventional prediction-based decision systems are, however, not reliable enough due to the uncertainties created by dynamic workloads, heterogeneity of resources, and changing network and resource contention. A predictive modeling framework is presented in this paper, which enables prediction with uncertainty, involves temporal feature engineering, telemetry gathering, probabilistic prediction, uncertainty estimation, risk-sensitive optimization, and adaptive feedback. The framework predicts workload demand, usage of resources, response time, service-level objective violation using hybrid machine-learning models. Uncertainty in prediction is measured using calibrated prediction intervals, by varying degrees of disagreement in an ensemble, and by quantile estimation. These estimates are used in making resource allocation, workload scheduling, service migration and task placement decisions with operational constraints. A fall-back mechanism provides conservative action in the presence of uncertainty above certain thresholds, with the confidence level to determine the appropriate action. Forecasting accuracy, interval coverage, decision latency, resource utilization, operational cost, and service-level compliance are among the impacts the proposed evaluation takes into account. Reproducible experiments are required to test the effectiveness, scalability and reliability of the framework in evolving distributed computing scenarios.

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