AI-Driven Orchestration of Telecom Workloads: Predictive Scaling and Anomaly Detection in Cloud-Native Networks
DOI:
https://doi.org/10.15662/IJARCST.2021.0406001Keywords:
AI-driven orchestration, Telecom workloads, Predictive scaling, Anomaly detection, Cloud-native networks, Resource optimization, Service resilience, Network automationAbstract
The evolution of telecom networks toward cloud-native architectures has created new opportunities and challenges in workload management, scalability, and reliability. Traditional static resource allocation often leads to inefficiencies, while reactive scaling strategies fail to meet the stringent performance and availability demands of telecom services. This research explores an AI-driven orchestration framework that integrates predictive scaling and anomaly detection to optimize telecom workloads in cloud-native environments. By leveraging machine learning models trained on historical traffic and system telemetry, the framework anticipates demand fluctuations and triggers proactive resource scaling. Simultaneously, anomaly detection mechanisms identify irregular patterns, enabling early fault isolation and mitigation. The proposed approach is validated through experimental simulations, demonstrating improved resource utilization, reduced latency, and enhanced service continuity compared to conventional orchestration methods. The results highlight the potential of AI to transform telecom workload management, ensuring resilient, adaptive, and efficient operations in next-generation networks.
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