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The improvements in the edge computing technology pave the road fordiversified applications that demand real-time interaction. However, due to themobility of the end-users and the dynamic edge environment, it becomeschallenging to handle the task offloading with high performance. Moreover,since each application in mobile devices has different characteristics, a taskorchestrator must be adaptive and have the ability to learn the dynamics of theenvironment. For this purpose, we develop a deep reinforcement learning basedtask orchestrator, DeepEdge, which learns to meet different task requirementswithout needing human interaction even under the heavily-loaded stochasticnetwork conditions in terms of mobile users and applications. Given the dynamicoffloading requests and time-varying communication conditions, we successfullymodel 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 undervarious loads. Furthermore, we compare the performance of our agent with thefour different task offloading approaches in the literature. Our results showthat DeepEdge outperforms its competitors in terms of the percentage ofsatisfactorily completed tasks.

DeepEdge: A Deep Reinforcement Learning based Task Orchestrator for Edge Computing