A Task Affinity Scheduling Framework Based on Multidimensional Resource Fingerprints

This study presents a task affinity scheduling framework that leverages multidimensional resource fingerprints to optimize resource management in cloud computing. The framework evaluates task affinity through complementarity and competitiveness analyses by constructing dynamic fingerprints from high-dimensional resource usage data. A novel algorithm captures temporal resource-usage patterns, enabling an adaptive scheduling strategy to improve resource utilization and performance. Experiments with real-world cloud traces demonstrated superior resource utilization, reduced task completion times, and improved quality of service under high-load conditions compared to existing methods. This approach offers a scalable solution for fine-grained resource management in a dynamic cloud environment.