Research on energy management of multi-user mobile edge computing offloading

In mobile edge computing system,the quality of computing experience can be improved greatly by offloading computing tasks from mobile devices to mobile edge computing servers.Consider incorporating renewable energy into a multi-user mobile edge system.Moreover,a battery as an energy harvesting devic...

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Main Authors: Luyao WANG, Wenqian ZHANG, Guanglin ZHANG
Format: Article
Language:zho
Published: China InfoCom Media Group 2019-03-01
Series:物联网学报
Subjects:
Online Access:http://www.wlwxb.com.cn/zh/article/doi/10.11959/j.issn.2096-3750.2019.00091/
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author Luyao WANG
Wenqian ZHANG
Guanglin ZHANG
author_facet Luyao WANG
Wenqian ZHANG
Guanglin ZHANG
author_sort Luyao WANG
collection DOAJ
description In mobile edge computing system,the quality of computing experience can be improved greatly by offloading computing tasks from mobile devices to mobile edge computing servers.Consider incorporating renewable energy into a multi-user mobile edge system.Moreover,a battery as an energy harvesting device was added to the model to harvest energy and storage.The task allocation strategy in mobile edge computing system was formulated through the resource management algorithm based on reinforcement learning,which achieved the cost minimization of mobile devices (including delay cost and computing cost).The simulation results show that the proposed algorithm significantly minimizes the cost of mobile devices compared with other algorithms.
format Article
id doaj-art-7519b3285f674667b83d7b876411257d
institution Kabale University
issn 2096-3750
language zho
publishDate 2019-03-01
publisher China InfoCom Media Group
record_format Article
series 物联网学报
spelling doaj-art-7519b3285f674667b83d7b876411257d2025-01-15T02:52:23ZzhoChina InfoCom Media Group物联网学报2096-37502019-03-013738159644285Research on energy management of multi-user mobile edge computing offloadingLuyao WANGWenqian ZHANGGuanglin ZHANGIn mobile edge computing system,the quality of computing experience can be improved greatly by offloading computing tasks from mobile devices to mobile edge computing servers.Consider incorporating renewable energy into a multi-user mobile edge system.Moreover,a battery as an energy harvesting device was added to the model to harvest energy and storage.The task allocation strategy in mobile edge computing system was formulated through the resource management algorithm based on reinforcement learning,which achieved the cost minimization of mobile devices (including delay cost and computing cost).The simulation results show that the proposed algorithm significantly minimizes the cost of mobile devices compared with other algorithms.http://www.wlwxb.com.cn/zh/article/doi/10.11959/j.issn.2096-3750.2019.00091/energy harvestingrenewable energymobile edge computingcost optimizationreinforcement learning
spellingShingle Luyao WANG
Wenqian ZHANG
Guanglin ZHANG
Research on energy management of multi-user mobile edge computing offloading
物联网学报
energy harvesting
renewable energy
mobile edge computing
cost optimization
reinforcement learning
title Research on energy management of multi-user mobile edge computing offloading
title_full Research on energy management of multi-user mobile edge computing offloading
title_fullStr Research on energy management of multi-user mobile edge computing offloading
title_full_unstemmed Research on energy management of multi-user mobile edge computing offloading
title_short Research on energy management of multi-user mobile edge computing offloading
title_sort research on energy management of multi user mobile edge computing offloading
topic energy harvesting
renewable energy
mobile edge computing
cost optimization
reinforcement learning
url http://www.wlwxb.com.cn/zh/article/doi/10.11959/j.issn.2096-3750.2019.00091/
work_keys_str_mv AT luyaowang researchonenergymanagementofmultiusermobileedgecomputingoffloading
AT wenqianzhang researchonenergymanagementofmultiusermobileedgecomputingoffloading
AT guanglinzhang researchonenergymanagementofmultiusermobileedgecomputingoffloading