Cloud-Native Security Optimization through Predictive AI-Based Workload Scheduling and Resource Management

Dr. Xiaolei Wang; Dr. Xiaolei Wang

doi:10.15662/ijetemr.2022.0706001

Cloud-Native Security Optimization through Predictive AI-Based Workload Scheduling and Resource Management

Dr. Xiaolei Wang

Department of Computer Science, School of Science, Aalto University, Finland

Abstract

Cloud-native computing has transformed modern application deployment through containerization, microservices, orchestration, and elastic resource allocation. However, the dynamic nature of cloud-native environments introduces significant security challenges, including resource contention, unpredictable workload behavior, unauthorized access, and vulnerabilities caused by inefficient scheduling decisions. Traditional workload scheduling mechanisms primarily optimize resource utilization, application performance, and service availability, often treating security as a separate operational concern. This paper investigates an integrated approach to cloud-native security optimization using predictive artificial intelligence (AI)-based workload scheduling and resource management. The proposed approach combines machine learning-based workload forecasting, risk-aware scheduling, continuous security monitoring, and adaptive resource allocation to improve infrastructure resilience. Historical workload metrics, runtime events, network traffic patterns, and security telemetry are analyzed to predict resource demand and identify potentially anomalous activities. The scheduling mechanism incorporates security risk, workload sensitivity, resource requirements, and service-level objectives into placement decisions. Furthermore, adaptive resource management supports proactive scaling and workload isolation while minimizing operational overhead. The proposed research methodology employs a simulation-based experimental framework to compare the integrated approach against conventional scheduling techniques using security, performance, and resource-efficiency metrics. The study establishes a structured foundation for evaluating how predictive intelligence can strengthen cloud-native security while maintaining scalability, availability, and operational efficiency in distributed computing environments

Article Information

Journal	International Journal of Emerging Trends in Engineering and Management Research
Volume (Issue)	Vol. 7 No. 6 (2022): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR)
DOI	https://doi.org/10.15662/ijetemr.2022.0706001
Pages	12881-12889
Published	November 20, 2022
Copyright	All rights reserved
Open Access	This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite	Dr. Xiaolei Wang (%2022). Cloud-Native Security Optimization through Predictive AI-Based Workload Scheduling and Resource Management. International Journal of Emerging Trends in Engineering and Management Research , Vol. 7 No. 6 (2022): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR) , pp. 12881-12889. https://doi.org/10.15662/ijetemr.2022.0706001

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