Enhancing Computational Offloading for Sustainable Smart Cities: A Deep Belief Network Approach
الموضوعات : Cloud computing
Kaebeh Yaeghoobi
1
,
Mahsa Bakhshandeh N
2
1 - Faculty of Computer Engineering, K. N. Toosi University of Technology, Tehran Iran
2 - Faculty of Engineering, Ale-Taha Institute of Higher Education, Tehran, Iran
الکلمات المفتاحية: Computational Offloading, Cloud Computing, Deep Belief Network, Response Time, Resource Management, Sustainable Smart Cities, Real-time Management,
ملخص المقالة :
The use of mobile devices with limited processing power has surged in recent years, alongside the expansion of cloud and fog computing across various sectors. These devices can handle small to medium computing tasks, but they fall short when it comes to large-scale processes, making computational offloading a crucial solution. Cloud computing and fog computing provide an effective platform for offloading tasks from mobile devices. However, critical real-time applications necessitate a near-edge approach to managing the computational load. Significant challenges exist in optimizing response times for effective offloading in cloud computing. This research introduces a framework for predicting response times using Deep Belief Network (DBN) learning to enhance offloading performance. Implementing a DBN aims to minimize response times and resource consumption, thereby improving the overall efficiency of offloading processes. The framework is designed to predict response times accurately, ensuring timely completion of tasks and efficient use of resources. Simulation results using multiple models show that the use of DBN significantly reduces processing, response, and offloading times compared to other algorithms. Consequently, the DBN algorithm proves to be more efficient in predicting response times and enhancing offloading performance. By leveraging the capabilities of DBN, this framework provides a promising solution for optimizing computational offloading in cloud computing environments. This enhances the performance of mobile devices and ensures the reliability and efficiency of real-time applications, direct the way for more advanced and responsive computing technologies.
[1] A. Mahdavi and A. Ghaffari, “Embedding Virtual Machines in Cloud Computing based on Big Bang–Big Crunch Algorithm,” Journal of Information Systems & Telecommunication (JIST), p. 305, 2019.
[2] N. Fernando, S. W. Loke, and W. Rahayu, “Mobile cloud computing: A survey,” Future Generation Computer Systems, vol. 29, no. 1, pp. 84–106, Jan. 2013, doi: 10.1016/J.FUTURE.2012.05.023.
[3] Md. G. R. Alam, M. M. Hassan, Md. Z. Uddin, A. S. Almogren, and G. Fortino, “Autonomic computation offloading in mobile edge for IoT applications,” Future Gener. Comput. Syst., vol. 90, pp. 149–157, 2019, [Online]. Available: https://api.semanticscholar.org/CorpusID:52899499.
[4] P. Boopathy et al., “Deep learning for intelligent demand response and smart grids: A comprehensive survey,” Comput Sci Rev, vol. 51, p. 100617, Feb. 2024, doi: 10.1016/J.COSREV.2024.100617.
[5] I. Abdullaev, N. Prodanova, K. A. Bhaskar, E. L. Lydia, S. Kadry, and J. Kim, “Task Offloading and Resource Allocation in IoT Based Mobile Edge Computing Using Deep Learning,” Computers, Materials and Continua, vol. 76, no. 2, pp. 1463–1477, Aug. 2023, doi: 10.32604/CMC.2023.038417.
[6] H. Naseri, S. Azizi, and A. Abdollahpouri, “BSFS: A Bidirectional Search Algorithm for Flow Scheduling in Cloud Data Centers,” Journal of Information Systems and Telecommunication (JIST), vol. 3, no. 27, p. 175, 2020.
[7] D. Seddiki, F. J. Maldonado Carrascosa, S. García Galán, M. Valverde Ibáñez, T. Marciniak, and N. Ruiz Reyes, “Enhanced virtual machine migration for energy sustainability optimization in cloud computing through knowledge acquisition,” Computers and Electrical Engineering, vol. 119, p. 109506, Oct. 2024, doi: 10.1016/J.COMPELECENG.2024.109506.
[8] L.-D. Chou, H.-F. Chen, F.-H. Tseng, H.-C. Chao, and Y.-J. Chang, “DPRA: Dynamic Power-Saving Resource Allocation for Cloud Data Center Using Particle Swarm Optimization,” IEEE Syst J, vol. 12, no. 2, pp. 1554–1565, 2018, doi: 10.1109/JSYST.2016.2596299.
[9] H. Wang, S. Cao, H. Li, L. Yan, Z. Guo, and Y. Gao, “Multi-objective joint optimization of task offloading based on MADRL in internet of things assisted by satellite networks,” Computer Networks, vol. 254, p. 110801, Dec. 2024, doi: 10.1016/J.COMNET.2024.110801.
[10] T. Tsokov and H. Kostadinov, “Dynamic network-aware container allocation in Cloud/Fog computing with mobile nodes,” Internet of Things, vol. 26, p. 101211, Jul. 2024, doi: 10.1016/J.IOT.2024.101211.
[11] C. You, K. Huang, H. Chae, and B.-H. Kim, “Energy-Efficient Resource Allocation for Mobile-Edge Computation Offloading,” IEEE Trans Wirel Commun, vol. 16, no. 3, pp. 1397–1411, 2017, doi: 10.1109/TWC.2016.2633522.
[12] Q. Wang, S. Guo, J. Liu, and Y. Yang, “Energy-efficient computation offloading and resource allocation for delay-sensitive mobile edge computing,” Sustainable Computing: Informatics and Systems, vol. 21, pp. 154–164, Mar. 2019, doi: 10.1016/J.SUSCOM.2019.01.007.
[13] S. C. Ghoshal et al., “VESBELT: An energy-efficient and low-latency aware task offloading in Maritime Internet-of-Things networks using ensemble neural networks,” Future Generation Computer Systems, vol. 161, pp. 572–585, Dec. 2024, doi: 10.1016/J.FUTURE.2024.07.034.
[14] S. Yang, D. Kwon, H. Yi, Y. Cho, Y. Kwon, and Y. Paek, “Techniques to Minimize State Transfer Costs for Dynamic Execution Offloading in Mobile Cloud Computing,” IEEE Trans Mob Comput, vol. 13, no. 11, pp. 2648–2660, 2014, doi: 10.1109/TMC.2014.2307293.
[15] X. Xu, Q. Huang, X. Yin, M. Abbasi, M. R. Khosravi, and L. Qi, “Intelligent Offloading for Collaborative Smart City Services in Edge Computing,” IEEE Internet Things J, vol. 7, no. 9, pp. 7919–7927, Sep. 2020, doi: 10.1109/JIOT.2020.3000871.
[16] T. Tang, C. Li, and F. Liu, “Collaborative cloud-edge-end task offloading with task dependency based on deep reinforcement learning,” Comput Commun, vol. 209, pp. 78–90, Sep. 2023, doi: 10.1016/J.COMCOM.2023.06.021.
[17] Y. Miao, G. Wu, M. Li, A. Ghoneim, M. Al-Rakhami, and M. S. Hossain, “Intelligent task prediction and computation offloading based on mobile-edge cloud computing,” Future Generation Computer Systems, vol. 102, pp. 925–931, Jan. 2020, doi: 10.1016/J.FUTURE.2019.09.035.
[18] M. Du, Y. Wang, K. Ye, and C. Xu, “Algorithmics of Cost-Driven Computation Offloading in the Edge-Cloud Environment,” IEEE Transactions on Computers, vol. 69, no. 10, pp. 1519–1532, 2020, doi: 10.1109/TC.2020.2976996.
[19] L. Tan, Z. Kuang, J. Gao, and L. Zhao, “Energy-Efficient Collaborative Multi-Access Edge Computing via Deep Reinforcement Learning,” IEEE Trans Industr Inform, vol. 19, no. 6, pp. 7689–7699, Jun. 2023, doi: 10.1109/TII.2022.3213603.
[20] A. Shakarami, A. Shahidinejad, and M. Ghobaei-Arani, “An autonomous computation offloading strategy in Mobile Edge Computing: A deep learning-based hybrid approach,” Journal of Network and Computer Applications, vol. 178, p. 102974, Mar. 2021, doi: 10.1016/J.JNCA.2021.102974.
[21] S. Zhong, S. Guo, H. Yu, and Q. Wang, “Cooperative service caching and computation offloading in multi-access edge computing,” Computer Networks, vol. 189, p. 107916, Apr. 2021, doi: 10.1016/J.COMNET.2021.107916.
[22] G. Peng, H. Wu, H. Wu, and K. Wolter, “Constrained Multiobjective Optimization for IoT-Enabled Computation Offloading in Collaborative Edge and Cloud Computing,” IEEE Internet Things J, vol. 8, no. 17, pp. 13723–13736, 2021, doi: 10.1109/JIOT.2021.3067732.
[23] Z. N. Samani and M. R. Khayyambashi, “Reliable resource allocation and fault tolerance in mobile cloud computing,” Journal of Information Systems and Telecommunication (JIST), vol. 7, no. 2, pp. 96–109, 2019.
[24] J. Long, Y. Luo, X. Zhu, E. Luo, and M. Huang, “Computation offloading through mobile vehicles in IoT-edge-cloud network,” EURASIP J Wirel Commun Netw, vol. 2020, no. 1, p. 244, 2020, doi: 10.1186/s13638-020-01848-5.
[25] L. Bracciale, M. Bonola, P. Loreti, G. Bianchi, R. Amici, and A. Rabuffi, “CRAWDAD dataset roma/taxi (v. 2014-07-17),” 2014.