  With the fast development of mobile edge computing (MEC), there is an
increasing demand for running complex applications on the edge. These complex
applications can be represented as workflows where task dependencies are
explicitly specified. To achieve better Quality of Service (QoS), for instance,
faster response time and lower energy consumption, computation offloading is
widely used in the MEC environment. However, many existing computation
offloading strategies only focus on independent computation tasks but overlook
the task dependencies. Meanwhile, most of these strategies are based on search
algorithms such as particle swarm optimization (PSO), genetic algorithm (GA)
which are often time-consuming and hence not suitable for many delay-sensitive
complex applications in MEC. Therefore, a highly efficient graph-based strategy
was proposed in our recent work but it can only deal with simple workflow
applications with linear (namely sequential) structure. For solving these
problems, a novel graph-based strategy is proposed for workflow applications in
MEC. Specifically, this strategy can deal with complex workflow applications
with nonlinear (viz. parallel, selective and iterative) structures. Meanwhile,
the offloading decision plan with the lowest energy consumption of the
end-device under the deadline constraint can be found by using the graph-based
partition technique. We have comprehensively evaluated our strategy using both
a real-world case study on a MEC based UAV (Unmanned Aerial Vehicle) delivery
system and extensive simulation experiments on the FogWorkflowSim platform for
MEC based workflow applications. The evaluation results successfully
demonstrate the effectiveness of our proposed strategy and its overall better
performance than other representative strategies.
