Tytuł artykułu
Identyfikatory
Warianty tytułu
Języki publikacji
Abstrakty
n the ever-evolving landscape of smart city applications and Intelligent Transport Systems, Vehicular Edge Computing emerged as a game-changing technology. Imagine a world where computational resources are no longer restricted to distant cloud servers but are brought nearer to the vehicles and users. Task offloading enables the computation in edge and cloud server. This proximity not only minimizes network latency but also enables a unfold of vehicles to process tasks at the edge, offering a swift and interactive response to the scenarios of applications with delay sensitivity. To deal with this constraint, an integrated methodology is utilized to enhance the offloading process. The proposed system integrates the Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). The integrated system optimizes task allocation by exploring the solution space effectively and ensuring efficient resource utilization while minimizing latency. In the evaluation, PSO+GA exhibits enhanced adaptability to varying task sizes, facilitating efficient offloading to the edge as needed. Energy efficiency varies between the algorithms, with PSO+GA generally showing minimal energy consumption. When compared to already existing algorithms such as Energy aware offloading, no offloading and random offloading, PSO+GA outperformed these algorithms in system performance and less energy consumption by a factor of 1.18.
Wydawca
Rocznik
Tom
Strony
371--380
Opis fizyczny
Bibliogr. 15 poz., fig., tab.
Twórcy
- PSG Institute of Technology and Applied Research, Coimbatore, Tamil Nadu, India
autor
- PSG Institute of Technology and Applied Research, Coimbatore, Tamil Nadu, India
Bibliografia
- 1. Liu L, Chen C, Pei Q, Maharjan S, Zhang Y. Vehicular edge computing and networking: A survey. Mobile Network Application. 2021; 26(3): 1145–68.
- 2. Shabariram CP, Shanthi N, Priya Ponnuswamy P. Computational offloading in vehicular edge computing using multiple agent-based deterministic policy gradient algorithm and generative adversarial networks. International Journal of Ad Hoc and Ubiquitous Computing. 2023; 44(4): 209–20.
- 3. Meneguette R, De Grande R, Ueyama J, Filho GPR, Madeira E. Vehicular edge computing: architecture, resource management, security, and challenges. ACM computing survey. 2023; 55(1): 1–46.
- 4. Geng L, Zhao H, Wang J, Kaushik A, Yuan S, Feng W. Deep-reinforcement-learning-based distributed computation offloading in vehicular edge computing networks. IEEE Internet of Things Journal. 2023; 10(14): 12416–33.
- 5. Raza S, Wang S, Ahmed M, Anwar MR. A survey on vehicular edge computing: architecture, applications, technical issues, and future directions. Wireless Communications and Mobile Computing. 2019; 2019: 1–19.
- 6. Qi W, Xia X, Wang H, Xing Y. A task partitioning and offloading scheme in vehicular edge computing networks. In: 2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall). IEEE; 2021.
- 7. Cao S, Liu D, Dai C, Wang C, Yang Y, Zhang W, Zheng, D. Reinforcement learning based tasks offloading in vehicular edge computing networks. Computer Networks. 2023; 234(109894): 109894.
- 8. Zhang Z, Zeng F. Efficient task allocation for computation offloading in vehicular edge computing. IEEE Internet of Things Journal. 2023; 10(6): 5595–606.
- 9. Maleki H, Başaran M, Durak-Ata L. Handover-enabled dynamic computation offloading for vehicular edge computing networks. IEEE Transaction on Vehicular Technology. 2023; 72(7): 9394–405.
- 10. Ismail L, Materwala H, Hassanein HS. QoS-SLA-aware adaptive genetic algorithm for multi-request offloading in integrated edge-cloud computing in Internet of vehicles. arXiv [cs.NI]. 2022.
- 11. Dai X, Xiao Z, Jiang H, Chen H, Min G, Dustdar S. A learning-based approach for vehicle-to-vehicle computation offloading. IEEE Internet of Things Journal. 2023; 10(8): 7244–58.
- 12. Singh S, Kim DH. Joint optimization of computation offloading and resource allocation in C-RAN with mobile edge computing using evolutionary algorithms. IEEE Access. 2023; 11: 112693–705.
- 13. Dai Y, Xu D, Maharjan S, Zhang Y. Joint load balancing and offloading in vehicular edge computing and networks. IEEE Internet of Things Journal. 2019; 6(3): 4377–87.
- 14. Bute MS, Fan P, Zhang L, Abbas F. An efficient distributed task offloading scheme for vehicular edge computing networks. IEEE Transaction on Vehicular Technology. 2021; 70(12): 13149–61.
- 15. Materwala H, Ismail L, Shubair RM, Buyya R. Energy-SLA-aware genetic algorithm for edge–cloud integrated computation offloading in vehicular networks. Future Generation Computing Systems. 2022; 135: 205–22.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-2646e5fb-dab0-4caa-937f-d1c3495a0998
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.