  The improvements in the edge computing technology pave the road for
diversified applications that demand real-time interaction. However, due to the
mobility of the end-users and the dynamic edge environment, it becomes
challenging to handle the task offloading with high performance. Moreover,
since each application in mobile devices has different characteristics, a task
orchestrator must be adaptive and have the ability to learn the dynamics of the
environment. For this purpose, we develop a deep reinforcement learning based
task orchestrator, DeepEdge, which learns to meet different task requirements
without needing human interaction even under the heavily-loaded stochastic
network conditions in terms of mobile users and applications. Given the dynamic
offloading requests and time-varying communication conditions, we successfully
model the problem as a Markov process and then apply the Double Deep Q-Network
(DDQN) algorithm to implement DeepEdge. To evaluate the robustness of DeepEdge,
we experiment with four different applications including image rendering,
infotainment, pervasive health, and augmented reality in the network under
various loads. Furthermore, we compare the performance of our agent with the
four different task offloading approaches in the literature. Our results show
that DeepEdge outperforms its competitors in terms of the percentage of
satisfactorily completed tasks.
