Hyperautomation and AI: The Next Evolution in Cloud Workload Management

Pradeep Kurra

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2025, vol. 11, issue 2, 3268-3278

Abstract: Hyperautomation and artificial intelligence are revolutionizing cloud workload management by transforming static, rule-based automation into dynamic, autonomous systems capable of self-learning and adaptation. This transformation addresses critical challenges in increasingly complex multi-cloud environments where traditional approaches have proven inadequate. Drawing on extensive empirical data from global implementations, hyperautomation demonstrates remarkable improvements across operational domains. By combining machine learning, advanced analytics, and process automation within unified frameworks, organizations achieve significant enhancements in resource utilization, fault detection, and automated remediation. The integration of AI with cloud-native technologies enables predictive scaling, intelligent workload placement, and autonomous incident resolution. Self-healing capabilities dramatically reduce downtime through automated anomaly detection and root cause analysis, while continuous learning mechanisms enable systems to evolve through operational experience. At the edge, AI-driven orchestration optimizes latency-sensitive applications through intelligent workload distribution and bandwidth optimization. In multi-cloud environments, hyperautomation provides unified management that transcends provider boundaries, enabling optimal resource allocation and consistent policy enforcement. The convergence of these capabilities with 5G networks further extends automation possibilities, creating a foundation for distributed intelligence across geographically dispersed resources.

Keywords: Hyperautomation; Artificial Intelligence; Cloud Workload Management; Self-Healing Systems; Edge Computing (search for similar items in EconPapers)
Date: 2025
Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT25112805
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Persistent link: https://EconPapers.repec.org/RePEc:jbh:ijsrcs:v11:y2025:i2:id:1372

DOI: 10.32628/CSEIT25112805

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