TIIS (한국인터넷정보학회)
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| 한글제목(Korean Title) |
A Context-aware Task Offloading Scheme in Collaborative Vehicular Edge Computing Systems |
| 영문제목(English Title) |
A Context-aware Task Offloading Scheme in Collaborative Vehicular Edge Computing Systems |
| 저자(Author) |
Zilong Jin
Chengbo Zhang
Guanzhe Zhao
Yuanfeng Jin
Lejun Zhang
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| 원문수록처(Citation) |
VOL 15 NO. 02 PP. 0383 ~ 0403 (2021. 02) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
With the development of mobile edge computing (MEC), some late-model application technologies, such as self-driving, augmented reality (AR) and traffic perception, emerge as the times require. Nevertheless, the high-latency and low-reliability of the traditional cloud computing solutions are difficult to meet the requirement of growing smart cars (SCs) with computing-intensive applications. Hence, this paper studies an efficient offloading decision and resource allocation scheme in collaborative vehicular edge computing networks with multiple SCs and multiple MEC servers to reduce latency. To solve this problem with effect, we propose a context-aware offloading strategy based on differential evolution algorithm (DE) by considering vehicle mobility, roadside units (RSUs) coverage, vehicle priority. On this basis, an autoregressive integrated moving average (ARIMA) model is employed to predict idle computing resources according to the base station traffic in different periods. Simulation results demonstrate that the practical performance of the context-aware vehicular task offloading (CAVTO) optimization scheme could reduce the system delay significantly.
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| 키워드(Keyword) |
Differential Evolution
Mobile Edge Computing
Machine Learning
Computing Offloading
Context-aware
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| 파일첨부 |
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