Journal
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
Volume 68, Issue 4, Pages 3061-3074Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TVT.2019.2895593
Keywords
Vehicular edge computing; task offloading; online learning; multi-armed bandit
Categories
Funding
- Nature Science Foundation of China [61871254, 91638204, 61571265, 61861136003, 61621091]
- National Key R&D Program of China [2018YFB0105005]
- NSF [CNS-1547461, CNS1718901, IIS-1838207]
- Intel Collaborative Research Institute for Intelligent and Automated Connected Vehicles
Ask authors/readers for more resources
The vehicular edge computing system integrates the computing resources of vehicles, and provides computing services for other vehicles and pedestrians with task offloading. However, the vehicular task offloading environment is dynamic and uncertain, with fast varying network topologies, wireless channel states, and computing workloads. These uncertainties bring extra challenges to task offloading. In this paper, we consider the task offloading among vehicles, and propose a solution that enables vehicles to learn the offloading delay performance of their neighboring vehicles while offloading computation tasks. We design an adaptive learning based task offloading (ALTO) algorithm based on the multi-armed bandit theory, in order to minimize the average offloading delay. ALTO works in a distributed manner without requiring frequent state exchange, and is augmented with input-awareness and occurrence-awareness to adapt to the dynamic environment. The proposed algorithm is proved to have a sublinear learning regret. Extensive simulations are carried out under both synthetic scenario and realistic highway scenario, and results illustrate that the proposed algorithm achieves low delay performance, and decreases the average delay up to 30% compared with the existing upper confidence bound based learning algorithm.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available