Multiple Workflows Offloading Based on Improved Deep Q-Network in Mobile Edge Computing

With the rapid development of the mobile Internet and the Internet of Things, there is an increasing need to execute compute-intensive tasks on mobile devices. But, mobile devices have limited resources, which makes it difficult to provide fast response times. In order to overcome such difficulties, a new computing paradigm called mobile edge computing (MEC) has been proposed to extend cloud-computing capabilities to the edge of the network. In MEC systems, mobile devices can offload compute-intensive tasks to resource-rich edge servers for execution, which effectively reduces the task processing latency. Nowadays, there are many researches on independent task offloading in MEC. However, many applications are composed of dependent tasks in real-world scenarios. Therefore, dependent task offloading has become a hot topic in MEC. In this paper, we study multiple workflows offloading in MEC. A model-free algorithm based on deep reinforcement learning is proposed to learn the optimal multiple workflows offloading strategy so as to minimize the number of workflows whose deadlines are not satisfied. Simulation results show that the proposed multiple workflows offloading strategy performs better than the state-ofthe-art approaches applied to similar problems.