Dynamic Offloading Framework in Fog Computing

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2022 by IJETT Journal
Volume-70 Issue-7
Year of Publication : 2022
Authors : Jyoti Yadav, Suman Sangwan
DOI : 10.14445/22315381/IJETT-V70I7P204

How to Cite?

Jyoti Yadav, Suman Sangwan, "Dynamic Offloading Framework in Fog Computing" International Journal of Engineering Trends and Technology, vol. 70, no. 7, pp. 32-42, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I7P204

Abstract
To meet the needs of Internet of Things (IoT) devices, fog computing has emerged as a new paradigm. Offloading computation tasks is one of the most crucial issues in a fog environment. Computation offloading is the process by which devices send computation-intensive tasks to servers for processing. Because of network constraints, not all computation tasks can be delegated to servers. As a result, it is critical to determine how many tasks should be run on servers and how many should be run locally. Furthermore, due to the uncertainty in the requirements, finding the server to execute the offloaded task in a vibrant environment is difficult. To address this issue, a dynamic computation offloading framework has been proposed. Here clustering is used to locate the decision engine, and fuzzy logic is used for offloading decisions. The major objective of the paper is to determine whether to offload or not depending on CPU usage, delay sensitivity, residual energy, and task size and bandwidth. Our algorithm makes dynamic decisions by sending time-sensitive tasks to local devices or fog nodes for processing and resource-intensive tasks to the cloud. According to simulation results, the proposed algorithm is more efficient than the Energy-aware offloading clustering approach (EAOCA) and the Fuzzy-based offloading algorithm regarding task successful execution, CPU utilization, and average delay. It improves the rate of successfully executed tasks by 5.92% over EAOCA and 4.72% over Fuzzy based approach. It reduces delay by 11.73% over EAOCA and 8.74 % over the Fuzzy approach.

Keywords
Fog computing, Clustering, Offloading, Delay, Fuzzy logic.

Reference
[1] A. Y. Joshi and P. S. Khanvilkar, "An Energy Efficient Workload Offloading in Fog Computing," pp. 5640–5645, 2020.
[2] W. Paper, "Prepare to succeed with the Internet of Things," pp. 1–9, 2017.
[3] Anu and A. Singhrova, "Optimal Healthcare Resource Allocation in Covid Scenario Using Firefly Algorithm," International Journal of Engineering Trends and Technology, vol. 70, no. 5, pp. 240–250, 2022.
[4] J. de J. Rugeles Uribe, E. P. Guillen, and L. S. Cardoso, "A technical review of wireless security for the internet of things: Software defined radio perspective," Journal of King Saud University - Computer and Information Sciences, no. xxxx, 2021.
[5] S. Shahhosseini et al., "Exploring computation offloading in IoT systems," Information Systems, no. xxxx, p. 101860, 2021.
[6] L. Zhang, Y. Liu, and S. Shen, "Construction of performance monitoring model for cloud computing service platform based on label technology," International Journal of Information and Communication Technology, vol. 17, no. 2, pp. 178–193, 2020.
[7] G. R. kumar, N. Saikiran, and A. Sathish, "FOG: A Novel Approach for Adapting IoT/IoE in Cloud Environment," International Journal of Engineering Trends and Technology, vol. 42, no. 4, pp. 189–192, 2016.
[8] D. Rahbari and M. Nickray, "Task offloading in mobile fog computing by classification and regression tree," Peer-to-Peer Networking and Applications, 2019.
[9] V. Meena, M. Gorripatti, and T. Suriya Praba, "Trust Enforced Computational Offloading for Health Care Applications in Fog Computing," Wireless Personal Communications, no. 0123456789, 2021.
[10] M. M. Hussain and M. M. S. Beg, "CODE-V: Multi-hop computation offloading in Vehicular Fog Computing," Future Sciences, no. 40, 2021.
[11] F. Yu, H. Chen, and J. Xu, "DMPO: Dynamic Mobility-Aware Partial Offloading in Mobile Edge Computing," Future Generation Computer Systems, vol. 89, pp. 722–735, 2018.
[12] Z. Ning, P. Dong, X. Kong, and F. Xia, "A Cooperative Partial Computation Offloading Scheme for Mobile Edge Computing Enabled Internet of Things," IEEE Internet of Things Journal, vol. 6, no. 3, pp. 4804–4814, 2019.
[13] Z. Li and Q. Zhu, "Genetic Algorithm-Based Optimization of Offloading and Resource Allocation in Mobile-Edge Computing," Information (Switzerland), vol. 11, no. 2, pp. 1–13, 2020.
[14] M. Babar, M. S. Khan, A. Din, F. Ali, U. Habib, and K. S. Kwak, "Intelligent Computation Offloading for IoT Applications in Scalable Edge Computing Using Artificial Bee Colony Optimization," Complexity, vol. 2021, pp. 1–12, 2021
[15] M. Keshavarznejad, "Delay-Aware Optimization of Energy Consumption for Task Offloading in Fog Environments Using Metaheuristic Algorithms," Cluster Computing, vol. 0123456789, 2021.
[16] S. Feng, Y. Chen, Q. Zhai, M. Huang, and F. Shu, "Optimizing Computation Offloading Strategy in Mobile Edge Computing Based on Swarm Intelligence Algorithms," Eurasip Journal on Advances in Signal Processing, vol. 2021, no. 1, 2021.
[17] H. Mahini and A. Masoud, "An Evolutionary Game Approach to Iot Task Offloading in Fog ‑ Cloud Computing," The Journal of Supercomputing, no. 0123456789, 2020.
[18] M. Adhikari, S. N. Srirama, and T. Amgoth, "Application Offloading Strategy for Hierarchical Fog Environment Through Swarm Optimization," IEEE Internet of Things Journal, vol. 7, no. 5, pp. 4317–4328, 2020.
[19] F. Sufyan and A. Banerjee, "Computation Offloading for Smart Devices in Fog-Cloud Queuing System," IETE Journal of Research, 2021.
[20] M. Adhikari and H. Gianey, "Energy Efficient Offloading Strategy in Fog-Cloud Environment for Iot Applications," Internet of Things, vol. 6, pp. 100053, 2019.
[21] A. Yousefpour, G. Ishigaki, R. Gour, and J. P. Jue, "On Reducing IoT Service Delay via Fog Offloading," IEEE Internet of Things Journal, vol. 5, no. 2, pp. 998–1010, 2018.
[22] M. G. R. Alam, M. M. Hassan, M. Zi. Uddin, A. Almogren, and G. Fortino, "Autonomic Computation Offloading in Mobile Edge for Iot Applications," Future Generation Computer Systems, vol. 90, pp. 149–157, 2019.
[23] N. Shan, Y. Li, and X. Cui, "A Multilevel Optimization Framework for Computation Offloading in Mobile Edge Computing,"Mathematical Problems in Engineering, vol. 2020, 2020.
[24] D. Santoro, D. Zozin, D. Pizzolli, F. De Pellegrini, and S. Cretti, "Foggy: A Platform for Workload Orchestration in a Fog Computing Environment," Proceedings of the International Conference on Cloud Computing Technology and Science, CloudCom, vol. 2017, pp. 231–234, 2017.
[25] N. Morkevicius, A. Venčkauskas, N. Šatkauskas, and J. Toldinas, “Method for Dynamic Service Orchestration in Fog Computing,” Electronics (Switzerland), vol. 10, no. 15, pp. 1–22, 2021.
[26] R. Mahmud, S. N. Srirama, K. Ramamohanarao, and R. Buyya, "Quality of Experience (QoE)-Aware Placement of Applications in Fog Computing Environments,"Journal of Parallel and Distributed Computing, vol. 132, pp. 190–203, 2019.
[27] H. Cheng, W. Xia, F. Yan, and L. Shen, "Balanced Clustering and Joint Resources Allocation in Cooperative Fog Computing System," pp. 1–6, 2019.
[28] A. Bozorgchenani, D. Tarchi, and G. E. Corazza, "An Energy-Aware Offloading Clustering Approach (EAOCA) in fog computing," Proceedings of the International Symposium on Wireless Communication Systems, vol. 2017, no. 8, pp. 390–395, 2017.
[29] E. Balevi and R. D. Gitlin, "A Clustering Algorithm that Maximizes Throughput in 5G Heterogeneous F-RAN Networks," IEEE International Conference on Communications, vol. 2018, 2018.
[30] A. Bozorgchenani, S. Disabato, D. Tarchi, and M. Roveri, "An energy Harvesting Solution for Computation Offloading in Fog Computing," Computer Communications, vol. 160, no. 3, pp. 577–587, 2020.
[31] N. M. Dhanya, G. Kousalya, P. Balarksihnan, and P. Raj, "Fuzzy-Logic-Based Decision Engine for Offloading Iot Application Using Fog Computing," Handbook of Research on Cloud and Fog Computing Infrastructures for Data Science, no. 5, pp. 175–194, 2018.
[32] W. Wibisono, M. Widhi, P. Putu, T. Ahmad, and R. Anggoro, "An Adaptive Offloading Framework for Improving Performance of Applications in IoT Devices Using Fuzzy Multi Criteria Decision Making," vol. 7, pp. 31–36, 2018.
[33] C. ge Wu, W. Li, L. Wang, and A. Y. Zomaya, "An Evolutionary Fuzzy Scheduler for Multi-Objective Resource Allocation in Fog Computing," Future Generation Computer Systems, vol. 117, pp. 498–509, 2021.
[34] P. Habibi, M. Farhoudi, S. Kazemian, S. Khorsandi, and A. Leon-Garcia, "Fog Computing: A Comprehensive Architectural Survey," IEEE Access, vol. 8, pp. 69105–69133, 2020.
[35] S. Trab, A. Zouinkhi, E. Bajic, M. N. Abdelkrim, and H. Chekir, "IoT-Based Risk Monitoring System for Safety Management in Warehouses," International Journal of Information and Communication Technology, vol. 13, no. 4, pp. 424–438, 2018.
[36] K. Khalid and E. N. Madi, "A Review of Computation Offloading for Mobile Cloud Computing Based on Fuzzy Set Theory," International Journal of Engineering Trends and Technology, no. 1, pp. 56–63, 2020.
[37] A. Kesarwani and P. M. Khilar, "Development of Trust Based Access Control Models Using Fuzzy Logic In Cloud Computing," Journal of King Saud University - Computer and Information Sciences, no. 40, 2019.
[38] C. Mechalikh, H. Taktak, and F. Moussa, "Towards a Scalable and QoS-Aware Load Balancing Platform for Edge Computing Environments," no. 7, 2019.
[39] M. Kumar and Suman, "Hybrid Cuckoo Search Algorithm for Scheduling in Cloud Computing," Computers, Materials and Continua, vol. 71, no. 1, pp. 1641–1660, 2022.