  In the new era of the Internet of Things (IoT), tasks are now being migrated
to edge sites closer to data generators. Mobile devices inherently encounter
limitations in terms of energy and computational processing capabilities. In
high mobility paradigm, ISAC provides a promising foundation for integrating
deployment management within dynamic spatial settings. We are interested in
applying prediction mechanism to resource allocation management by extracting
data attributes, focusing on ISAC related contexts of the trajectory and
velocity and making the allocating decision. We present a system design, a
theoretical framework and an implementation of the ClusterMan software package.
The numerical suggests that the strong clustering subset of feature may yield
high accuracy up to 97\% in the prediction results.
