Smart Resource Allocation for Mobile Edge Computing: A Deep Reinforcement Learning Approach

The development of mobile devices with improving communication and perceptual capabilities has brought about a proliferation of numerous complex and computation-intensive mobile applications. Mobile devices with limited resources face more severe capacity constraints than ever before. As a new concept of network architecture and an extension of cloud computing, Mobile Edge Computing (MEC) seems to be a promising solution to meet this emerging challenge. However, MEC also has some limitations, such as the high cost of infrastructure deployment and maintenance, as well as the severe pressure that the complex and mutative edge computing environment brings to MEC servers. At this point, how to allocate computing resources and network resources rationally to satisfy the requirements of mobile devices under the changeable MEC conditions has become a great aporia. To combat this issue, we propose a smart, Deep Reinforcement Learning based Resource Allocation (DRLRA) scheme, which can allocate computing and network resources adaptively, reduce the average service time and balance the use of resources under varying MEC environment. Experimental results show that the proposed DRLRA performs better than the traditional OSPF algorithm in the mutative MEC conditions.

Automated summary

The development of mobile devices has led to an increase in complex and computation-intensive mobile applications. Mobile Edge Computing (MEC) is a promising solution to the capacity constraints of mobile devices, but it also faces challenges such as high infrastructure costs and pressure on MEC servers. The proposed Deep Reinforcement Learning based Resource Allocation (DRLRA) scheme aims to adaptively allocate resources to improve service times and balance resource usage in changing MEC environments.