Resilient Enterprise Operations Using Predictive AI for Intelligent Workload Optimization Across Cloud Native Platforms
DOI:
https://doi.org/10.15680/IJMRSETM.2026.0207008Keywords:
Predictive AI, Cloud-Native Platforms, Workload Optimization, Intelligent Resource Management, Machine Learning, Kubernetes, Enterprise Resilience, Predictive Scaling, Cloud Computing, Resource AllocationAbstract
Cloud-native platforms have become fundamental to modern enterprise operations by providing scalable, flexible, and distributed computing capabilities through containers, Kubernetes, microservices, serverless services, and elastic cloud infrastructure. However, dynamically changing workloads, resource contention, unpredictable demand, infrastructure failures, and inefficient resource allocation can significantly affect application performance and operational resilience. This research proposes a predictive artificial intelligence framework for intelligent workload optimization across cloud-native enterprise platforms. The proposed approach integrates machine learning, workload forecasting, resource utilization analytics, anomaly detection, and automated optimization to predict future workload conditions and dynamically allocate computational resources. Enterprise telemetry, including CPU utilization, memory consumption, network throughput, request rates, application latency, container behavior, pod performance, and historical workload patterns, is analyzed to develop predictive models. These models estimate future resource requirements and identify potential performance degradation or capacity constraints before they affect critical applications. The framework incorporates predictive scaling, workload placement, resource prioritization, and intelligent scheduling to improve infrastructure utilization while maintaining service-level objectives. The methodology evaluates predictive accuracy, resource efficiency, response latency, workload stability, scalability, and resilience under variable operating conditions. By combining predictive intelligence with cloud-native orchestration, the proposed framework seeks to reduce resource wastage, prevent performance bottlenecks, improve application availability, and support resilient enterprise operations. The research provides a systematic foundation for adaptive and intelligent workload management capable of responding proactively to changing enterprise computing requirements.References
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