To meet the requirements of various computing intensive and delay-sensitive applications in vehicular networks, it is considered an effective approach to transmit the tasks of mobile users to nearby mobile edge computing (MEC) servers through computation offloading. In such a vehicular edge computing network, in addition to MEC resources, vehicles are also equipped with edge computing capabilities by carrying a cloudlet platform. Therefore, this paper designs a collaborative vehicular edge computing framework to realize the collaborative processing of computing-intensive and delay-sensitive tasks on resource-rich vehicular cloudlets. Based on this framework, multiple vehicular cloudlets can form a vehicular coalition and simultaneously process tasks of mobile users, thereby further improving the computing efficiency of vehicular edge computing networks. We formulate the collaborative task processing problem as a combinatorial auction with the objective of minimizing the total task processing cost of all mobile users. Moreover, a low-complexity greedy-based collaborative computing algorithm is proposed as our solution. Numerical results show that compared with the benchmark schemes, the proposed scheme performs better in minimizing the total task processing cost.
Collaborative Task Processing in Vehicular Edge Computing Networks
25.11.2021
982811 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch