HCOME: Research on Hybrid Computation Offloading Strategy for MEC Based on DDPG

With the growth of the Internet of Things, smart devices are subsequently generating a large number of computation-intensive and latency-sensitive tasks. Mobile edge computing can provide resources in close proximity, greatly reducing service latency and alleviating congestion in mobile core network...

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Main Authors: Shaohua Cao, Shu Chen, Hui Chen, Hanqing Zhang, Zijun Zhan, Weishan Zhang
Format: Article
Language:English
Published: MDPI AG 2023-01-01
Series:Electronics
Subjects:
Online Access:https://www.mdpi.com/2079-9292/12/3/562
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author Shaohua Cao
Shu Chen
Hui Chen
Hanqing Zhang
Zijun Zhan
Weishan Zhang
author_facet Shaohua Cao
Shu Chen
Hui Chen
Hanqing Zhang
Zijun Zhan
Weishan Zhang
author_sort Shaohua Cao
collection DOAJ
description With the growth of the Internet of Things, smart devices are subsequently generating a large number of computation-intensive and latency-sensitive tasks. Mobile edge computing can provide resources in close proximity, greatly reducing service latency and alleviating congestion in mobile core networks. Due to the instability of the mobile edge computing environment, it was difficult to guarantee the quality of service for users. To address this problem, a hybrid computation offloading framework based on Deep Deterministic Policy Gradient (DDPG) in IoT is proposed. The framework is a system consisting of edge servers and user devices. It is used to acquire the environment state through Software Defined Network technologies and generate the offloading strategy by Deep Deterministic Policy Gradient. The optimization objectives in this paper include the total system overhead of the mobile edge computing system, and considering both network load and computational load, an optimal offloading strategy can be obtained to enable users to obtain a better quality of service. Finally, the experimental results show that the algorithm outperforms the comparison algorithm and can reduce the system latency by 20%, while the network load and computational load are also more stable.
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spelling doaj.art-1b6f51e9e65d40b3bff1cd3764575d0b2023-11-16T16:28:14ZengMDPI AGElectronics2079-92922023-01-0112356210.3390/electronics12030562HCOME: Research on Hybrid Computation Offloading Strategy for MEC Based on DDPGShaohua Cao0Shu Chen1Hui Chen2Hanqing Zhang3Zijun Zhan4Weishan Zhang5Qingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, ChinaQingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, ChinaQingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, ChinaQingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, ChinaQingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, ChinaQingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, ChinaWith the growth of the Internet of Things, smart devices are subsequently generating a large number of computation-intensive and latency-sensitive tasks. Mobile edge computing can provide resources in close proximity, greatly reducing service latency and alleviating congestion in mobile core networks. Due to the instability of the mobile edge computing environment, it was difficult to guarantee the quality of service for users. To address this problem, a hybrid computation offloading framework based on Deep Deterministic Policy Gradient (DDPG) in IoT is proposed. The framework is a system consisting of edge servers and user devices. It is used to acquire the environment state through Software Defined Network technologies and generate the offloading strategy by Deep Deterministic Policy Gradient. The optimization objectives in this paper include the total system overhead of the mobile edge computing system, and considering both network load and computational load, an optimal offloading strategy can be obtained to enable users to obtain a better quality of service. Finally, the experimental results show that the algorithm outperforms the comparison algorithm and can reduce the system latency by 20%, while the network load and computational load are also more stable.https://www.mdpi.com/2079-9292/12/3/562mobile edge computingsoftware defined networkdeep reinforcement learninghybrid computation offloadingload balancing
spellingShingle Shaohua Cao
Shu Chen
Hui Chen
Hanqing Zhang
Zijun Zhan
Weishan Zhang
HCOME: Research on Hybrid Computation Offloading Strategy for MEC Based on DDPG
Electronics
mobile edge computing
software defined network
deep reinforcement learning
hybrid computation offloading
load balancing
title HCOME: Research on Hybrid Computation Offloading Strategy for MEC Based on DDPG
title_full HCOME: Research on Hybrid Computation Offloading Strategy for MEC Based on DDPG
title_fullStr HCOME: Research on Hybrid Computation Offloading Strategy for MEC Based on DDPG
title_full_unstemmed HCOME: Research on Hybrid Computation Offloading Strategy for MEC Based on DDPG
title_short HCOME: Research on Hybrid Computation Offloading Strategy for MEC Based on DDPG
title_sort hcome research on hybrid computation offloading strategy for mec based on ddpg
topic mobile edge computing
software defined network
deep reinforcement learning
hybrid computation offloading
load balancing
url https://www.mdpi.com/2079-9292/12/3/562
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AT hanqingzhang hcomeresearchonhybridcomputationoffloadingstrategyformecbasedonddpg
AT zijunzhan hcomeresearchonhybridcomputationoffloadingstrategyformecbasedonddpg
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