Intelligent Resource Coordination for Low-Latency Processing in Distributed Computing Systems

  • Authors

    • Faruck Mohammed Research Analyst, India. Author

    DOI:

    https://doi.org/10.67228/30715717/IJDEIC-V9I2P101

    Published 04-06-2026

  • Intelligent Resource Coordination, Low-Latency Computing, Distributed Systems, Adaptive Scheduling, Workload Prediction, Edge Computing, Cloud Computing, Task Placement, Load Balancing, Resource Optimization

    Issue

    Vol. 9 No. 2 (2026)

    Section

    Articles

  • Abstract

    The rapid growth of real-time digital services has increased the need for efficient resource management in distributed computing environments. Applications such as autonomous vehicles, industrial IoT, financial transactions, healthcare monitoring, online gaming, and AI require low latency, reliable service quality, and optimal resource utilization. However, traditional resource allocation methods struggle with fluctuating workloads, network congestion, and heterogeneous hardware. This paper proposes an intelligent resource coordination framework that integrates workload prediction, adaptive resource allocation, task prioritization, network-aware placement, and continuous performance monitoring. A multi-objective optimization model balances latency, throughput, resource utilization, energy consumption, and service-level agreement compliance. The framework is evaluated conceptually against static allocation and conventional load balancing under varying workloads and network conditions. Two analytical tables and two architectural figures illustrate the proposed design and evaluation criteria. As this study presents a research framework rather than completed experimental results, expected behaviors are distinguished from empirical findings. The study also identifies future research needs involving scheduling overhead, prediction uncertainty, scalability, fairness, and cross-layer coordination.

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