Dynamic Computation Offloading with Deep Reinforcement Learning in Edge Network

With the booming proliferation of user requests in the Internet of Things (IoT) network, Edge Computing (EC) is emerging as a promising paradigm for the provision of flexible and reliable services. Considering the resource constraints of IoT devices, for some delay-aware user requests, a heavy-workload IoT device may not respond on time. EC has sparked a popular wave of offloading user requests to edge servers at the edge of the network. The orchestration of user-requested offloading schemes creates a remarkable challenge regarding the delay in user requests and the energy consumption of IoT devices in edge networks. To solve this challenge, we propose a dynamic computation offloading strategy consisting of the following: (i) we propose the concept of intermediate nodes, which can minimize the delay in user requests and the energy consumption of the current tasks handled by IoT devices by dynamically combining task-offloading and service migration strategies; (ii) based on the workload of the current network, the intermediate node selection problem is modeled as a multi-dimensional Markov Decision Process (MDP) space, and a deep reinforcement learning algorithm is implemented to reduce the large MDP space and make a fast decision. Experimental results show that this strategy is superior to the existing baseline methods to reduce delays in user requests and the energy consumption of IoT devices.