• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

A Context-aware Task Offloading Scheme in Collaborative Vehicular Edge Computing Systems


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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Cite this article

[IEEE Style]
Z. Jin, C. Zhang, G. Zhao, Y. Jin and L. Zhang, "A Context-aware Task Offloading Scheme in Collaborative Vehicular Edge Computing Systems," KSII Transactions on Internet and Information Systems, vol. 15, no. 2, pp. 383-403, 2021. DOI: 10.3837/tiis.2021.02.001.

[ACM Style]
Zilong Jin, Chengbo Zhang, Guanzhe Zhao, Yuanfeng Jin, and Lejun Zhang. 2021. A Context-aware Task Offloading Scheme in Collaborative Vehicular Edge Computing Systems. KSII Transactions on Internet and Information Systems, 15, 2, (2021), 383-403. DOI: 10.3837/tiis.2021.02.001.