  This paper presents our preliminary results with ABEONA, an edge-to-cloud
architecture that allows migrating tasks from low-energy, resource-constrained
devices on the edge up to the cloud. Our preliminary results on artificial and
real world datasets show that it is possible to execute workloads in a more
efficient manner energy-wise by scaling horizontally at the edge, without
negatively affecting the execution runtime.
