| Autor: |
Salman Raza, Wei Liu, Manzoor Ahmed, Muhammad Rizwan Anwar, Muhammad Ayzed Mirza, Qibo Sun, Shangguang Wang |
| Jazyk: |
angličtina |
| Rok vydání: |
2020 |
| Předmět: |
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| Zdroj: |
Journal of Cloud Computing: Advances, Systems and Applications, Vol 9, Iss 1, Pp 1-14 (2020) |
| Druh dokumentu: |
article |
| ISSN: |
2192-113X |
| DOI: |
10.1186/s13677-020-00175-w |
| Popis: |
Abstract Vehicular edge computing (VEC) is a promising paradigm to offload resource-intensive tasks at the network edge. Owing to time-sensitive and computation-intensive vehicular applications and high mobility scenarios, cost-efficient task offloading in the vehicular environment is still a challenging problem. In this paper, we study the partial task offloading problem in vehicular edge computing in an urban scenario. Where the vehicle computes some part of a task locally, and offload the remaining task to a nearby vehicle and to VEC server subject to the maximum tolerable delay and vehicle’s stay time. To make it cost-efficient, including the cost of the required communication and computing resources, we consider to fully exploit the vehicular available resources. We estimate the transmission rates for the vehicle to vehicle and vehicle to infrastructure communication based on practical assumptions. Moreover, we present a mobility-aware partial task offloading algorithm, taking into account the task allocation ratio among the three parts given by the communication environment conditions. Simulation results validate the efficient performance of the proposed scheme that not only enhances the exploitation of vehicular computation resources but also minimizes the overall system cost in comparison to baseline schemes. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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