Statistics

Markov decision process

Operating system

Distributed computing

Mathematical optimization

Edge device

Artificial intelligence

Markov process

Enhanced Data Rates for GSM Evolution

Economics

Q-learning

Mobile edge computing

Convergence (economics)

Edge computing

Resource management (computing)

Resource allocation

Computer network

Mathematics

Computer science

Economic growth

Cloud computing

Reinforcement learning

Process (computing)

IEEE Journal on Selected Areas in Communications

By leveraging mobile edge computing (MEC), a huge amount of data generated by Internet of Things (IoT) devices can be processed and analyzed at the network edge. However, the MEC system usually only has the limited virtual resources, which are shared and competed by IoT edge applications. Thus, we propose a resource allocation policy for the IoT edge computing system to improve the efficiency of resource utilization. The objective of the proposed policy is to minimize the long-term weighted sum of average completion time of jobs and average number of requested resources. The resource allocation problem in the MEC system is formulated as a Markov decision process (MDP). A deep reinforcement learning approach is applied to solve the problem. We also propose an improved deep Q-network (DQN) algorithm to learn the policy, where multiple replay memories are applied to separately store the experiences with small mutual influence. Simulation results show that the proposed algorithm has a better convergence performance than the original DQN algorithm, and the corresponding policy outperforms the other reference policies by lower completion time with fewer requested resources.

Resource Allocation Based on Deep Reinforcement Learning in IoT Edge Computing