Mixed-Timescale Joint Computational Offloading and Wireless Resource Allocation Strategy in Energy Harvesting Multi-MEC Server Systems
As an emerging paradigm enabling mobile devices to leverage additional computation resources from nearby MEC servers (MSs), mobile edge computing (MEC) has drawn great attention from academia to industry. Unlike the conventional cloud server, the MEC provides a medium-scale and portable computation...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8732365/ |
| _version_ | 1818665613032161280 |
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| author | Yinglei Teng Kang Cheng Yong Zhang Xianbin Wang |
| author_facet | Yinglei Teng Kang Cheng Yong Zhang Xianbin Wang |
| author_sort | Yinglei Teng |
| collection | DOAJ |
| description | As an emerging paradigm enabling mobile devices to leverage additional computation resources from nearby MEC servers (MSs), mobile edge computing (MEC) has drawn great attention from academia to industry. Unlike the conventional cloud server, the MEC provides a medium-scale and portable computation ability at MSs without relying on the time-consuming and capacity-constrained backhaul. However, the MEC offloading process is still highly sensitive to the fluctuation of both radio and computing resources. In this paper, considering the independent variation of the wireless channel conditions and computing tasks, we propose a Mixed-timescale Joint Computational offloading and Wireless resource allocation (MJCW) algorithm for latency-critical applications, aiming at minimizing the total energy consumption. Through such a new approach, the original NP-hard problem is decoupled into a short-term stage problem seeking for the allocation of physical power and subcarrier and a long-term stage problem of task offloading and frequency scaling. The simulation results show that the proposed algorithm achieves excellent performance in energy saving in comparison with conventional schemes and realizes higher utilization of green energy by adjusting the energy price of MSs. |
| first_indexed | 2024-12-17T05:51:25Z |
| format | Article |
| id | doaj.art-7eaab5b578f24e6eb92c2329dfd09476 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-17T05:51:25Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-7eaab5b578f24e6eb92c2329dfd094762022-12-21T22:01:08ZengIEEEIEEE Access2169-35362019-01-017746407465210.1109/ACCESS.2019.29213178732365Mixed-Timescale Joint Computational Offloading and Wireless Resource Allocation Strategy in Energy Harvesting Multi-MEC Server SystemsYinglei Teng0https://orcid.org/0000-0002-7170-4764Kang Cheng1Yong Zhang2https://orcid.org/0000-0003-4997-698XXianbin Wang3Beijing Key Laboratory of Space-ground Interconnection and Convergence, Beijing University of Posts and Telecommunications, Beijing, ChinaE-surfing Internet of Things Technology Co., Ltd., Nanjing, ChinaBeijing Key Laboratory of Space-ground Interconnection and Convergence, Beijing University of Posts and Telecommunications, Beijing, ChinaDepartment of ECE, Western University, London, ON, CanadaAs an emerging paradigm enabling mobile devices to leverage additional computation resources from nearby MEC servers (MSs), mobile edge computing (MEC) has drawn great attention from academia to industry. Unlike the conventional cloud server, the MEC provides a medium-scale and portable computation ability at MSs without relying on the time-consuming and capacity-constrained backhaul. However, the MEC offloading process is still highly sensitive to the fluctuation of both radio and computing resources. In this paper, considering the independent variation of the wireless channel conditions and computing tasks, we propose a Mixed-timescale Joint Computational offloading and Wireless resource allocation (MJCW) algorithm for latency-critical applications, aiming at minimizing the total energy consumption. Through such a new approach, the original NP-hard problem is decoupled into a short-term stage problem seeking for the allocation of physical power and subcarrier and a long-term stage problem of task offloading and frequency scaling. The simulation results show that the proposed algorithm achieves excellent performance in energy saving in comparison with conventional schemes and realizes higher utilization of green energy by adjusting the energy price of MSs.https://ieeexplore.ieee.org/document/8732365/Mobile computingenergy efficiencyenergy harvestingtask offloading |
| spellingShingle | Yinglei Teng Kang Cheng Yong Zhang Xianbin Wang Mixed-Timescale Joint Computational Offloading and Wireless Resource Allocation Strategy in Energy Harvesting Multi-MEC Server Systems IEEE Access Mobile computing energy efficiency energy harvesting task offloading |
| title | Mixed-Timescale Joint Computational Offloading and Wireless Resource Allocation Strategy in Energy Harvesting Multi-MEC Server Systems |
| title_full | Mixed-Timescale Joint Computational Offloading and Wireless Resource Allocation Strategy in Energy Harvesting Multi-MEC Server Systems |
| title_fullStr | Mixed-Timescale Joint Computational Offloading and Wireless Resource Allocation Strategy in Energy Harvesting Multi-MEC Server Systems |
| title_full_unstemmed | Mixed-Timescale Joint Computational Offloading and Wireless Resource Allocation Strategy in Energy Harvesting Multi-MEC Server Systems |
| title_short | Mixed-Timescale Joint Computational Offloading and Wireless Resource Allocation Strategy in Energy Harvesting Multi-MEC Server Systems |
| title_sort | mixed timescale joint computational offloading and wireless resource allocation strategy in energy harvesting multi mec server systems |
| topic | Mobile computing energy efficiency energy harvesting task offloading |
| url | https://ieeexplore.ieee.org/document/8732365/ |
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