  Internet of Things (IoT) is considered as the enabling platform for a variety
of promising applications, such as smart transportation and smart city, where
massive devices are interconnected for data collection and processing. These
IoT applications pose a high demand on storage and computing capacity, while
the IoT devices are usually resource-constrained. As a potential solution,
mobile edge computing (MEC) deploys cloud resources in the proximity of IoT
devices so that their requests can be better served locally. In this work, we
investigate computation offloading in a dynamic MEC system with multiple edge
servers, where computational tasks with various requirements are dynamically
generated by IoT devices and offloaded to MEC servers in a time-varying
operating environment (e.g., channel condition changes over time). The
objective of this work is to maximize the completed tasks before their
respective deadlines and minimize energy consumption. To this end, we propose
an end-to-end Deep Reinforcement Learning (DRL) approach to select the best
edge server for offloading and allocate the optimal computational resource such
that the expected long-term utility is maximized. The simulation results are
provided to demonstrate that the proposed approach outperforms the existing
methods.
