Spherical Stochastic Mapping

This study proposes a novel algorithm that transitions the allocation of hardware resources such as CPU, storage, and memory, as well as containers, in cloud environment from a traditional planar coordinate-based placement to a probabilistic mapping based on a spherical coordinate [1] system.To achieve efficient clustering of resources with diverse performance characteristics, the algorithm maps the dynamic attributes of resources to a probabilistic distribution on the sphere and performs resource allocation with optimal cost.This approach enables the formation of an ideal task clustering that considers both resource characteristics and dynamic system changes.