Enhancing the User Experience in Vehicular Edge Computing Networks: An Adaptive Resource Allocation Approach
Mobile edge computing (MEC) has been developed as a key technique to handle the explosive computation demands of vehicles. However, it is non-trivial to realize high-reliable and low-latency vehicular requirements among distributed and capacity-constrained MEC nodes. Besides, the dynamic and uncerta...
| 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/8889668/ |
| _version_ | 1818735890463195136 |
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| author | Xiaoke Sun Junhui Zhao Xiaoting Ma Qiuping Li |
| author_facet | Xiaoke Sun Junhui Zhao Xiaoting Ma Qiuping Li |
| author_sort | Xiaoke Sun |
| collection | DOAJ |
| description | Mobile edge computing (MEC) has been developed as a key technique to handle the explosive computation demands of vehicles. However, it is non-trivial to realize high-reliable and low-latency vehicular requirements among distributed and capacity-constrained MEC nodes. Besides, the dynamic and uncertain vehicular environments bring extra challenges to preserve the long-term satisfactory user experience. In this paper, an adaptive resource allocation approach is investigated to enhance the user experience in vehicular edge computing networks. Specifically, leveraging the idea of task scalability, a model for balancing computing quality and resource consumption is introduced to exploit the computational resources fully. Towards the goal of minimizing the long-term computing quality loss by specifying the needed resource and the expected quality of each running task, a mix-integer non-linear stochastic optimization problem is formulated to jointly optimize the allocation of radio and computing resources, as well as the task placement. Due to the unpredictable network states and the high computational complexity of the formulated problem, the long-term optimization problem is firstly decomposed into a series of one-slot problems, and then, an iterative algorithm is provided to derive a computation efficient solution. Finally, both rigorous theoretical analysis and extensive trace-driven simulations validate the efficacy of our proposed approach. |
| first_indexed | 2024-12-18T00:28:27Z |
| format | Article |
| id | doaj.art-b608a41bbe81480987c3cd319c5293a2 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-18T00:28:27Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-b608a41bbe81480987c3cd319c5293a22022-12-21T21:27:11ZengIEEEIEEE Access2169-35362019-01-01716107416108710.1109/ACCESS.2019.29508988889668Enhancing the User Experience in Vehicular Edge Computing Networks: An Adaptive Resource Allocation ApproachXiaoke Sun0https://orcid.org/0000-0003-2940-7026Junhui Zhao1https://orcid.org/0000-0003-4290-086XXiaoting Ma2https://orcid.org/0000-0002-5558-4256Qiuping Li3https://orcid.org/0000-0002-5370-8606School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, ChinaSchool of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, ChinaSchool of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, ChinaSchool of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, ChinaMobile edge computing (MEC) has been developed as a key technique to handle the explosive computation demands of vehicles. However, it is non-trivial to realize high-reliable and low-latency vehicular requirements among distributed and capacity-constrained MEC nodes. Besides, the dynamic and uncertain vehicular environments bring extra challenges to preserve the long-term satisfactory user experience. In this paper, an adaptive resource allocation approach is investigated to enhance the user experience in vehicular edge computing networks. Specifically, leveraging the idea of task scalability, a model for balancing computing quality and resource consumption is introduced to exploit the computational resources fully. Towards the goal of minimizing the long-term computing quality loss by specifying the needed resource and the expected quality of each running task, a mix-integer non-linear stochastic optimization problem is formulated to jointly optimize the allocation of radio and computing resources, as well as the task placement. Due to the unpredictable network states and the high computational complexity of the formulated problem, the long-term optimization problem is firstly decomposed into a series of one-slot problems, and then, an iterative algorithm is provided to derive a computation efficient solution. Finally, both rigorous theoretical analysis and extensive trace-driven simulations validate the efficacy of our proposed approach.https://ieeexplore.ieee.org/document/8889668/Vehicular edge computinglong-term user experiencecomputing quality optimizationadaptive resource allocation |
| spellingShingle | Xiaoke Sun Junhui Zhao Xiaoting Ma Qiuping Li Enhancing the User Experience in Vehicular Edge Computing Networks: An Adaptive Resource Allocation Approach IEEE Access Vehicular edge computing long-term user experience computing quality optimization adaptive resource allocation |
| title | Enhancing the User Experience in Vehicular Edge Computing Networks: An Adaptive Resource Allocation Approach |
| title_full | Enhancing the User Experience in Vehicular Edge Computing Networks: An Adaptive Resource Allocation Approach |
| title_fullStr | Enhancing the User Experience in Vehicular Edge Computing Networks: An Adaptive Resource Allocation Approach |
| title_full_unstemmed | Enhancing the User Experience in Vehicular Edge Computing Networks: An Adaptive Resource Allocation Approach |
| title_short | Enhancing the User Experience in Vehicular Edge Computing Networks: An Adaptive Resource Allocation Approach |
| title_sort | enhancing the user experience in vehicular edge computing networks an adaptive resource allocation approach |
| topic | Vehicular edge computing long-term user experience computing quality optimization adaptive resource allocation |
| url | https://ieeexplore.ieee.org/document/8889668/ |
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