Abstract

The rapid evolution of cloud computing has underscored the need for scalable and efficient container orchestration. As organizations increasingly adopt containerized applications to achieve agility and portability, the optimization of container scheduling becomes critical for resource utilization, service reliability, and cost efficiency. This research presents an intelligent container scheduling strategy tailored for cloud environments, integrating resource-aware algorithms and real-time performance metrics to allocate containers dynamically. The proposed approach reduces idle resource fragmentation, balances workload across heterogeneous nodes, and adapts to failures through fault-tolerant mechanisms. Experimental analysis using Docker Swarm demonstrates significant improvement in throughput, reduced latency, and enhanced fault recovery compared to traditional scheduling models. The findings highlight the importance of adaptive, context-aware scheduling policies in advancing cloud-native infrastructure efficiency.

References

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