5G Converged Network Resource Allocation Strategy Based on Reinforcement Learning in Edge Cloud Computing Environment
Open Access
- 14 May 2022
- journal article
- research article
- Published by Hindawi Limited in Computational Intelligence and Neuroscience
- Vol. 2022, 1-8
- https://doi.org/10.1155/2022/6174708
Abstract
Aiming at the problem that computing power and resources of Mobile Edge Computing (MEC) servers are difficult to process long-period intensive task data, this study proposes a 5G converged network resource allocation strategy based on reinforcement learning in edge cloud computing environment. n order to solve the problem of insufficient local computing power, the proposed strategy offloads some tasks to the edge of network. Firstly, we build a multi-MEC server and multi-user mobile edge system, and design optimization objectives to minimize the average response time of system tasks and total energy consumption. Then, task offloading and resource allocation process is modeled as Markov decision process. Furthermore, the deep Q-network is used to find the optimal resource allocation scheme. Finally, the proposed strategy is analyzed experimentally based on TensorFlow learning framework. Experimental results show that when the number of users is 110, final energy consumption is about 2500J, which effectively reduces task delay and improves the utilization of resources.Keywords
Funding Information
- The 2019 Anhui Province University Outstanding Top Talent Cultivation Funding Project (gxgnfx2019050, 202101207023, 2020SJSFJXZZ417)
This publication has 25 references indexed in Scilit:
- Efficient Resource Allocation for Mobile-Edge Computing Networks With NOMA: Completion Time and Energy MinimizationIEEE Transactions on Communications, 2019
- Assessing the Energy Consumption of Proactive Mobile Edge Caching in Wireless NetworksIEEE Access, 2019
- Radio and computing resource allocation with energy harvesting devices in mobile edge computing environmentComputer Communications, 2019
- Efficient Mobility-Aware Task Offloading for Vehicular Edge Computing NetworksIEEE Access, 2019
- Mobile Edge Computing With Wireless Backhaul: Joint Task Offloading and Resource AllocationIEEE Access, 2019
- Dynamic resource allocation strategy for latency-critical and computation-intensive applications in cloud–edge environmentComputer Communications, 2019
- Communication and computation cooperation in cloud radio access network with mobile edge computingCCF Transactions on Networking, 2018
- Computation Offloading and Resource Allocation in Vehicular Networks Based on Dual-Side Cost MinimizationIEEE Transactions on Vehicular Technology, 2018
- Energy-Aware Computation Offloading and Transmit Power Allocation in Ultradense IoT NetworksIEEE Internet of Things Journal, 2018
- Contract-theoretic Approach for Delay Constrained Offloading in Vehicular Edge Computing NetworksMobile Networks and Applications, 2018