  Edge Computing (EC) is about remodeling the way data is handled, processed,
and delivered within a vast heterogeneous network. One of the fundamental
concepts of EC is to push the data processing near the edge by exploiting
front-end devices with powerful computation capabilities. Thus, limiting the
use of centralized architecture, such as cloud computing, to only when it is
necessary. This paper proposes a novel edge computer offloading technique that
assigns computational tasks generated by devices to potential edge computers
with enough computational resources. The proposed approach clusters the edge
computers based on their hardware specifications. Afterwards, the tasks
generated by devices will be fed to a hybrid Artificial Neural Network (ANN)
model that predicts, based on these tasks, the profiles, i.e., features, of the
edge computers with enough computational resources to execute them. The
predicted edge computers are then assigned to the cluster they belong to so
that each task is assigned to a cluster of edge computers. Finally, we choose
for each task the edge computer that is expected to provide the fastest
response time. The experiment results show that our proposed approach
outperforms other state-of-the-art machine learning approaches using real-world
IoT dataset.
