期刊
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
卷 23, 期 12, 页码 25536-25545出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TITS.2021.3091321
关键词
Cloud computing; Resource management; Optimization; Vehicle dynamics; Computational modeling; Transportation; Task analysis; Multi-objective optimization; vehicular cloud computing; resource allocation; NSGA-II
资金
- National Key Research and Development Program of China [2018YFE0202800, 2020YFB1807500, 2018YFC0831200]
- National Natural Science Foundation of China [62072360, 61571338, 61672131, 61901367]
- Natural Science Foundation of Shaanxi Province for Distinguished Young Scholars [2020JC-26]
- Youth Innovation Team of Shaanxi Universities
- Key Laboratory of Embedded System and Service Computing (Tongji University) [ESSCKF2019-05]
- Ministry of Education
- Xi'an Science and Technology Plan [20RGZN0005]
- Xi'an Key Laboratory of Mobile Edge Computing and Security [201805052-ZD3CG36]
- Fundamental Research Funds for the Central Universities [31412111303, 31512111310]
- State Key Laboratory for Novel Software Technology, Nanjing University [KFKT2019B17]
Modern transportation faces challenges in safety, mobility, environment, and space limitations. Vehicular networks are seen as a promising solution to improve transportation satisfaction and convenience. This paper focuses on resource allocation in vehicular cloud computing, using an enhanced genetic algorithm to achieve better performance.
Modern transportation is associated with considerable challenges related to safety, mobility, the environment and space limitations. Vehicular networks are widely considered to be a promising approach for improving satisfaction and convenience in transportation. However, with the exploding popularity among vehicle users and the growing diverse demands of different services, ensuring the efficient use of resources and meeting the emerging needs remain challenging. In this paper, we focus on resource allocation in vehicular cloud computing (VCC) and fill the gaps in the previous research by optimizing resource allocation from both the provider's and users' perspectives. We model this problem as a multi-objective optimization with constraints that aims to maximize the acceptance rate and minimize the provider's cloud cost. To solve such an NP-hard problem, we improve the nondominated sorting genetic algorithm II (NSGA-II) by modifying the initial population according to the matching factor, dynamic crossover probability and mutation probability to promote excellent individuals and increase population diversity. The simulation results show that our proposed method achieves enhanced performance compared to the previous methods.
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