Intelligent Network Resource Optimization Using Predictive Analytics in Cloud-Connected Systems

  • Authors

    • Shakila Begum Research Scholar, India. Author

    DOI:

    https://doi.org/10.67228/3071642X/IJCFDE-V9I3P104

    Published 07-11-2026

  • Predictive Analytics, Cloud-Connected Systems, Intelligent Networking, Resource Optimization, Traffic Forecasting, Quality of Service, Machine Learning, Bandwidth Allocation, Congestion Prediction, Adaptive Resource Management

    Issue

    Vol. 9 No. 3 (2026)

    Section

    Articles

  • Abstract

    Cloud-connected systems enable a wide range of applications with the need for dependable connectivity, increasing computing power, and effective network resources usage. But dynamic workloads, varying traffic patterns, diverse devices and unpredictable service demands make traditional resource allocation schemes more complex. This paper proposes an intelligent network resource optimization framework using predictive analytics that predict traffic change and allocate network resources proactively. The suggested approach combines telemetry data collection, temporal feature engineering, workload forecasting, adaptive resource allocation and feedback-driven optimization. The bandwidth demand, congestion probability and resource utilization is estimated by predictive models; the allocation controller can therefore adjust the bandwidth, the routing preferences as well as the computing capacity based on the expected service requirements. A multi-objective optimization formulation is used that involves latency, throughput, packet loss, resource utilization and operational cost, while meeting QoS constraints. The evaluation approach involves comparing the two resource management strategies—predictive and threshold-based—using percentage-based performance metrics and workload traces.The evaluation approach is through the use of consistent workload traces and percentage-based performance metrics in comparing the two RM strategies—predictive and threshold-based. The framework focuses on the accuracy of the forecast, stability of allocation, scalability, and resilience of the network in different scenarios. Its design offers a systematic basis for optimizing the use of resources in cloud connected systems without compromising the reliability of services and enabling data-driven and adaptive network management.

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