  The demand for distributed applications has significantly increased over the
past decade, with improvements in machine learning techniques fueling this
growth. These applications predominantly utilize Cloud data centers for
high-performance computing and Fog and Edge devices for low-latency
communication for small-size machine learning model training and inference. The
challenge of executing applications with different requirements on
heterogeneous devices requires effective methods for solving NP-hard resource
allocation and application scheduling problems. The state-of-the-art techniques
primarily investigate conflicting objectives, such as the completion time,
energy consumption, and economic cost of application execution on the Cloud,
Fog, and Edge computing infrastructure. Therefore, in this work, we review
these research works considering their objectives, methods, and evaluation
tools. Based on the review, we provide a discussion on the scheduling methods
in the Computing Continuum.
