  Cloud Robotics is helping to create a new generation of robots that leverage
the nearly unlimited resources of large data centers (i.e., the cloud),
overcoming the limitations imposed by on-board resources. Different processing
power, capabilities, resource sizes, energy consumption, and so forth, make
scheduling and task allocation critical components. The basic idea of task
allocation and scheduling is to optimize performance by minimizing completion
time, energy consumption, delays between two consecutive tasks, along with
others, and maximizing resource utilization, number of completed tasks in a
given time interval, and suchlike. In the past, several works have addressed
various aspects of task allocation and scheduling. In this paper, we provide a
comprehensive overview of task allocation and scheduling strategies and related
metrics suitable for robotic network cloud systems. We discuss the issues
related to allocation and scheduling methods and the limitations that need to
be overcome. The literature review is organized according to three different
viewpoints: Architectures and Applications, Methods and Parameters. In
addition, the limitations of each method are highlighted for future research.
