Abstract
Resource discovery is a crucial component in high-performance computing (HPC) systems. This paper presents a multi-agent model for resource discovery in distributed exascale systems. Agents are categorized based on resource types and behavior-specific characteristics. The model enables efficient identification and acquisition of memory, process, file, and IO resources. Through a comprehensive exploration, we highlight the potential of our approach in addressing resource discovery challenges in exascale computing systems, paving the way for optimized resource utilization and enhanced system performance.
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