Predictive Resource Planning For Energy-Efficient Cloud Computing and Enterprise Workload Management

  • Authors

    • Karthick Quality Analyst, USA. Author

    DOI:

    https://doi.org/10.67228/30715725/IJIARE-V9I3P103

    Published 09-23-2026

  • Cloud Computing, Predictive Resource Planning, Energy-Efficient Computing, Workload Forecasting, Resource Allocation, Intelligent Scheduling, Enterprise Workload Management, Sustainable Data Centers

    Issue

    Vol. 9 No. 3 (2026)

    Section

    Articles

  • Abstract

    This paper proposes a conceptual framework for predictive resource planning in enterprise cloud computing to improve resource utilization, energy efficiency, and application performance. Traditional resource management approaches often fail to anticipate workload variations, leading to higher costs, resource wastage, and performance issues. The proposed framework integrates workload forecasting, energy-aware resource allocation, intelligent scheduling, and continuous performance monitoring using historical data and real-time system information. Its effectiveness will be evaluated using energy consumption, resource utilization, SLA compliance, prediction accuracy, response latency, and operational costs. The study provides a foundation for developing sustainable and intelligent cloud management systems, with empirical validation required to confirm its effectiveness.

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