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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China InfoCom Media Group
2019-03-01
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Series: | 物联网学报 |
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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 |