Smart City Gnosys
Smart city article details
| Title | Latency-Aware Batch Task Offloading For Vehicular Cloud: Maximizing Submodular Bandit |
|---|---|
| ID_Doc | 34794 |
| Authors | Li H.; Huang H.; Qian Z. |
| Year | 2021 |
| Published | IEEE International Conference on Cloud Computing, CLOUD, 2021-September |
| DOI | http://dx.doi.org/10.1109/CLOUD53861.2021.00076 |
| Abstract | The concept and application of smart city brings us enormous computing resources, but it also brings considerable computing heterogeneity. In this paper, we propose a brand new scenario of batch task offloading for vehicular cloud which maximizes the performance of offloading strategy in case of satisfying the deadline restraints and reliability requirements. To capture the sources of the service delay better, we split the task offloading process into several components, and focus on the optimization of routing and task execution. We model our problem (Batch Task Offloading for Vehicle Cloud problem with reliability restraints) in a submodular function maximizing perspective and show its NP-hardness. We present a novel greedy algorithm for offline scenario and analyze its theoretical performance. We prove our algorithm is \left( {1-{1 \over e}} \right) -Approximate in polynomial time. Furthermore, we study the problem in the online scenario using bandit submodular set maximizing model. We present an algorithm with regret bound O(\text{log}{2}\ T) running in a small amount of arms which can reduce the computing complexity and the number of selected arms under the same performance. We also evaluate our algorithm in a microscopic and continuous traffic simulation platform, SUMO. The result shows our algorithms (both online and offline) outperforms other existing algorithms over 20%. © 2021 IEEE. |
| Author Keywords | Edge computing; Submodular Function Maximizing; Task offloading |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
19899  | 0.86 | Strauss T.; Oechsle M.; Bauknecht U. | Differentiable Optimization For Orchestration: Resource Offloading For Vehicles In Smart Cities | IEEE Access, 12 (2024) |
| 57451 | 0.859 | Chen X.; Zhang L.; Pang Y.; Lin B.; Fang Y. | Timeliness-Aware Incentive Mechanism For Vehicular Crowdsourcing In Smart Cities | IEEE Transactions on Mobile Computing, 21, 9 (2022) |
| 37269 | 0.858 | Enfang Z.; Li P.; Zhang T.; Huang W.; Liu Q. | Mobile Data Offloading In The Vehicular Networks: A Truthful Incentive Mechanism | Proceedings - 21st IEEE International Conference on High Performance Computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2019 (2019) |
| 18051 | 0.856 | Agbaje P.; Nwafor E.; Olufowobi H. | Deep Reinforcement Learning For Energy-Efficient Task Offloading In Cooperative Vehicular Edge Networks | IEEE International Conference on Industrial Informatics (INDIN), 2023-July (2023) |
| 8798 | 0.856 | Liu W.; Shao X.; Wang C.; Gu X.; Jiang F.; Peng J. | An Online Reinforcement Learning Offloading Method For Delay-Sensitive Vehicular Service | Proceedings - 2020 IEEE 22nd International Conference on High Performance Computing and Communications, IEEE 18th International Conference on Smart City and IEEE 6th International Conference on Data Science and Systems, HPCC-SmartCity-DSS 2020 (2020) |
| 54436 | 0.853 | Shabariram C.P.; Ponnuswamy P.P. | Task Offloading In Edge Computing Using Integrated Particle Swarm Optimization And Genetic Algorithm | Advances in Science and Technology Research Journal, 19, 1 (2025) |