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http://dx.doi.org/10.3837/tiis.2021.02.001

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

Jin, Zilong (School of Computer and Software, Nanjing University of Information Science and Technology)
Zhang, Chengbo (School of Computer and Software, Nanjing University of Information Science and Technology)
Zhao, Guanzhe (Huihua College of Hebei Normal University)
Jin, Yuanfeng (Department of Physics, Yanbian University)
Zhang, Lejun (College of Information Engineering, Yangzhou University)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.15, no.2, 2021 , pp. 383-403 More about this Journal
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.
Keywords
Differential Evolution; Mobile Edge Computing; Machine Learning; Computing Offloading; Context-aware;
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