What question did this study set out to answer?

The aim is to explore machine learning methods for optimizing cloud workload scheduling amidst increasing complexity.

April 8, 2026Open Access

Machine Learning For Cloud Workload Scheduling Optimization

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This review analyzes machine learning-driven optimization techniques for cloud workload scheduling, indicating their effectiveness and challenges.

The aim is to explore machine learning methods for optimizing cloud workload scheduling amidst increasing complexity.
Reviewed historical transition from static to machine learning-based scheduling systems.
Categorized contemporary machine learning techniques used in scheduling.
Evaluated performance based on multi-objective criteria such as energy efficiency and SLA compliance.
Identified critical bottlenecks such as model drift and interpretability.
Showed advancements in autonomous frameworks for workload scheduling.
Highlighted recent developments in agent-based artificial intelligence for resource management.

Nimal Perera (2025) studied this question.

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This review analyzes machine learning-driven optimization techniques for cloud workload scheduling, indicating their effectiveness and challenges.