Vehicular Networks will play a crucial role in future Intelligent Transportation Systems (ITS). Due to the limited computing capacity of the vehicles, a certain number of data jobs could be offloaded to external servers. However, offloading to servers in remote clouds is not possible due to latency requirements of some applications or if generated jobs are too "big"(big data). For this reason, thanks to 5G technology and Multi-Access Edge Computing (MEC), it is possible to offload jobs to servers placed at the edge of the network, realizing the Vehicular Edge Computing (VEC). The aim of this paper is to define a Decision Making Scheme for computation offloading, with the objective of minimizing job offloading costs, while respecting some constraints in terms of processing delay and loss probability. Some numerical results are presented to demonstrate the performance of the proposed solution.
Decision making optimization for job offloading in vehicular edge computing networks
Grasso C.;Schembra G.
2020-01-01
Abstract
Vehicular Networks will play a crucial role in future Intelligent Transportation Systems (ITS). Due to the limited computing capacity of the vehicles, a certain number of data jobs could be offloaded to external servers. However, offloading to servers in remote clouds is not possible due to latency requirements of some applications or if generated jobs are too "big"(big data). For this reason, thanks to 5G technology and Multi-Access Edge Computing (MEC), it is possible to offload jobs to servers placed at the edge of the network, realizing the Vehicular Edge Computing (VEC). The aim of this paper is to define a Decision Making Scheme for computation offloading, with the objective of minimizing job offloading costs, while respecting some constraints in terms of processing delay and loss probability. Some numerical results are presented to demonstrate the performance of the proposed solution.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
