Energy-Latency Aware Offloading for Hierarchical Mobile Edge Computing
Mobile edge computing (MEC) enhances the computing capacity of resources-poor user equipment (UE) by computational offloading. However, edge clouds suffer from a limited computation capacity, and thus cannot cater for a large amount of offloading requests in periods of high load. To tackle this issu...
| 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/8819989/ |
| _version_ | 1819179111265861632 |
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| author | Binwei Wu Jie Zeng Lu Ge Xin Su Youxi Tang |
| author_facet | Binwei Wu Jie Zeng Lu Ge Xin Su Youxi Tang |
| author_sort | Binwei Wu |
| collection | DOAJ |
| description | Mobile edge computing (MEC) enhances the computing capacity of resources-poor user equipment (UE) by computational offloading. However, edge clouds suffer from a limited computation capacity, and thus cannot cater for a large amount of offloading requests in periods of high load. To tackle this issue, the hierarchical MEC network is proposed and can utilize the vast resources in the backhaul and backbone networks. Previous studies describe the network layout with a three-tier tree which is not suitable for the realistic implementation. Meanwhile, the influences brought by network congestion on backhaul and backbone links are omitted. Thus, we generalize the assumption on the network layout and propose topology-independent offloading algorithms which can balance the workload over the entire region of the MEC network. In order to relieve the congestion on the backhaul and backbone networks, the task routing is incorporated into the offloading optimization, along with the offloading decision, the transmission power control, and the cloud selection. In order to jointly conduct the offloading optimization, we convert the offloading problem into a multi-source single-destination routing. A distributed offloading approach (i.e., BROA) is developed based on the game theory, in which UE collaborates with each other to minimize the network cost in terms of energy consumption and latency. We theoretically analyze the efficiency of UE collaboration and prove that BROA can achieve the globally optimal solution. Furthermore, an approximate offloading algorithm (i.e., FCOA) is developed which can give a quick solution to adapt to time-varying environments. We theoretically demonstrate the convergence, the accuracy, and the time complexity of FCOA. Numerical results show that the proposed algorithms are superior to conventional offloading schemes. |
| first_indexed | 2024-12-22T21:53:15Z |
| format | Article |
| id | doaj.art-4891c902af824654823254f10b962a8d |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-22T21:53:15Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-4891c902af824654823254f10b962a8d2022-12-21T18:11:18ZengIEEEIEEE Access2169-35362019-01-01712198212199710.1109/ACCESS.2019.29381868819989Energy-Latency Aware Offloading for Hierarchical Mobile Edge ComputingBinwei Wu0https://orcid.org/0000-0001-7354-7902Jie Zeng1https://orcid.org/0000-0003-4486-5041Lu Ge2Xin Su3Youxi Tang4National Key Laboratory of Science and Technology on Communications, University of Electronic Science and Technology of China, Chengdu, ChinaBeijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, ChinaBeijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, ChinaBeijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, ChinaNational Key Laboratory of Science and Technology on Communications, University of Electronic Science and Technology of China, Chengdu, ChinaMobile edge computing (MEC) enhances the computing capacity of resources-poor user equipment (UE) by computational offloading. However, edge clouds suffer from a limited computation capacity, and thus cannot cater for a large amount of offloading requests in periods of high load. To tackle this issue, the hierarchical MEC network is proposed and can utilize the vast resources in the backhaul and backbone networks. Previous studies describe the network layout with a three-tier tree which is not suitable for the realistic implementation. Meanwhile, the influences brought by network congestion on backhaul and backbone links are omitted. Thus, we generalize the assumption on the network layout and propose topology-independent offloading algorithms which can balance the workload over the entire region of the MEC network. In order to relieve the congestion on the backhaul and backbone networks, the task routing is incorporated into the offloading optimization, along with the offloading decision, the transmission power control, and the cloud selection. In order to jointly conduct the offloading optimization, we convert the offloading problem into a multi-source single-destination routing. A distributed offloading approach (i.e., BROA) is developed based on the game theory, in which UE collaborates with each other to minimize the network cost in terms of energy consumption and latency. We theoretically analyze the efficiency of UE collaboration and prove that BROA can achieve the globally optimal solution. Furthermore, an approximate offloading algorithm (i.e., FCOA) is developed which can give a quick solution to adapt to time-varying environments. We theoretically demonstrate the convergence, the accuracy, and the time complexity of FCOA. Numerical results show that the proposed algorithms are superior to conventional offloading schemes.https://ieeexplore.ieee.org/document/8819989/Computation offloadinggame theorygeneralized network layouthierarchical mobile edge computing (MEC) network |
| spellingShingle | Binwei Wu Jie Zeng Lu Ge Xin Su Youxi Tang Energy-Latency Aware Offloading for Hierarchical Mobile Edge Computing IEEE Access Computation offloading game theory generalized network layout hierarchical mobile edge computing (MEC) network |
| title | Energy-Latency Aware Offloading for Hierarchical Mobile Edge Computing |
| title_full | Energy-Latency Aware Offloading for Hierarchical Mobile Edge Computing |
| title_fullStr | Energy-Latency Aware Offloading for Hierarchical Mobile Edge Computing |
| title_full_unstemmed | Energy-Latency Aware Offloading for Hierarchical Mobile Edge Computing |
| title_short | Energy-Latency Aware Offloading for Hierarchical Mobile Edge Computing |
| title_sort | energy latency aware offloading for hierarchical mobile edge computing |
| topic | Computation offloading game theory generalized network layout hierarchical mobile edge computing (MEC) network |
| url | https://ieeexplore.ieee.org/document/8819989/ |
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