Cloud-Native Scheduling and Resource Orchestration: A Deep Dive into AI-Driven Approaches

Cloud-native computing has transformed modern applica-
tion development, deployment, and management by enabling scalability
and flexibility. However, the increasing complexity of workloads and dy-
namic resource demands challenge traditional scheduling and resource
provisioning techniques, often leading to inefficiencies. This paper ex-
plores AI-driven approaches to optimizing cloud-native scheduling and
resource provisioning. By leveraging machine learning, deep reinforce-
ment learning, and predictive analytics, AI enhances decision-making,
automates scaling, and improves workload distribution. We present a
comprehensive review of recent AI techniques applied to container or-
chestration, and Kubernetes-based scheduling, analyzing their impact
on cost reduction, performance optimization, and resource efficiency.
Additionally, we discuss key challenges such as model interpretability,
real-time adaptability, and integration with existing cloud and edge in-
frastructures. Ultimately, this paper provides insights into the future of
intelligent cloud and edge resource management, emphasizing the neces-
sity of AI-augmented strategies to meet the growing demands of next-
generation applications.