How to Cite:
Nalini Sivakumar, "Predictive Resource Allocation for High-Velocity Data Streams in Cloud Environments" International Journal of Humanities Science Innovations and Management Studies, Vol. 1, No. 1, pp. 14-21, 2026.

Abstract:
The rapid growth of Internet of Things (IoT) devices, financial transactions, industrial monitoring systems, social platforms, and real-time analytics has created an increasing demand for cloud infrastructures capable of processing high-velocity data streams with low latency and predictable performance. Conventional cloud resource allocation approaches primarily rely on reactive scaling, in which computational resources are increased after workload demand exceeds predefined thresholds. Although effective for relatively stable workloads, reactive approaches can introduce provisioning delays, resource over-allocation, performance degradation, and increased operational cost when stream arrival rates change rapidly. This research proposes a predictive resource allocation framework for high-velocity data streams in cloud environments. The proposed approach integrates real-time stream monitoring, workload forecasting, resource-demand estimation, adaptive scaling, and feedback-based optimization into a unified control architecture. Historical and short-term streaming characteristics are analyzed to predict future workload intensity and estimate the required compute, memory, network, and processing capacity. The framework incorporates predictive scaling before anticipated workload peaks while continuously adjusting resource allocation according to observed runtime conditions. A methodological comparison with static and reactive allocation strategies demonstrates how predictive allocation can improve resource utilization, reduce scaling latency, and maintain service-level objectives under bursty workloads. The study also identifies important challenges involving forecasting uncertainty, heterogeneous workloads, multi-tenant interference, model drift, and the cost of maintaining prediction infrastructure. The proposed architecture provides a foundation for intelligent cloud resource management in latency-sensitive streaming applications.

Keywords: Predictive Resource Allocation, High-Velocity Data Streams, Cloud Computing, Stream Processing, Workload Forecasting, Autoscaling, Resource Optimization, Predictive Scaling, Real-Time Analytics.

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