Name
Abstract | ||
|---|---|---|
In vehicular edge computing (VEC), offloading the tasks to the nearby resource-rich edge servers helps each vehicle enhance computational capabilities and improve in-vehicle applications' performance. However, the concentration of travel at specific spaces and times poses significant challenges on the load-balancing and scheduling of computation tasks at the edge servers. This paper studies a low-complexity dynamic online offloading strategy that efficiently reduces task delay and computing resource consumption in the multi-user, multiserver vehicular edge computing scenarios. Our design addresses issues of computation task placement and execution order of the tasks on each server. We use a realistic approach that vehicles generate tasks over time, and the set of the tasks is unknown in advance so that the offloading decisions are made in run-time. Extensive simulations are conducted on a real mobility trace of Luxembourg city, and the results show that the proposed algorithm effectively improves the offloading utility of the system. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/VTC2021-FALL52928.2021.9625245 | 2021 IEEE 94TH VEHICULAR TECHNOLOGY CONFERENCE (VTC2021-FALL) |
Keywords | DocType | ISSN |
Vehicular networks, Edge computing, Off-loading, Connected vehicles | Conference | 2577-2465 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hong Duc Nguyen | 1 | 0 | 0.34 |
| Shunsuke Aoki | 2 | 0 | 0.68 |
| Yuuki Nishiyama | 3 | 0 | 1.35 |
| Kaoru Sezaki | 4 | 5 | 1.79 |