Multi-Objective Predictive Optimization for Latency-Aware and Energy-Efficient Intelligent Computing

  • Authors

    • Shakila Begum Research Scholar, India. Author

    DOI:

    https://doi.org/10.67228/30715725/IJIARE-V9I2P101

    Published 04-06-2026

  • Multi-Objective Optimization, Predictive Analytics, Intelligent Computing, Latency-Aware Scheduling, Energy Efficiency, Edge Computing, Cloud Computing, Adaptive Resource Allocation, Machine Learning, Pareto Optimization

    Issue

    Vol. 9 No. 2 (2026)

    Section

    Articles

  • Abstract

    The growing adoption of AI, IoT, edge computing, and cloud-native services has increased the need for computing architectures that deliver low latency, high performance, and energy efficiency. Traditional resource allocation methods often optimize individual metrics while overlooking workload variability and the relationships between latency, energy consumption, and resource availability. This paper proposes a conceptual framework for multi-objective predictive optimization that integrates workload forecasting, performance modeling, energy estimation, adaptive resource allocation, and decision-making. Using predictive analytics, constrained optimization, and Pareto-based selection, the framework balances latency and energy consumption across heterogeneous CPU, GPU, edge, and cloud resources while maintaining service-level requirements and computational reliability. A reproducible evaluation strategy based on workload traces, simulations, and baseline comparisons is proposed. As no experimental results are available, the discussion focuses on analytical comparisons and projected performance implications rather than fabricated measurements. The study also highlights challenges involving prediction accuracy, optimization overhead, workload uncertainty, and scalability, providing a foundation for future research into sustainable and adaptive intelligent computing systems.

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