Journal of Soft Computing and Information Technology

Journal of Soft Computing and Information Technology

A resource allocation method for fog computing considering user mobility

Document Type : Persian Original Article

Authors
Samira Ansari Moghaddam 1
Mehri Rajaei 2
Samira Noferesti 3
1 Department of electronic and computer engineering university of systan and baluchestan
2 Department of Information technology, Faculty of electronic and computer engineering, University of Sistan and Baluchestan ,Zahedan,Iran
3 Information Technology Department, Faculty of Electrical and Computer Engineering, University of Sistan and Baluchestan, Zahedan, Iran.
Abstract
With the growing use of mobile devices and delay-sensitive applications, the need to move computing power to the proximity of users increases compared to cloud computing. However, fog nodes have limited computing and storage capacity and, therefore, do not perform well under congestion of requests. One of the causes of congestion and loss of user requests is the mobility of users across fog node areas. The purpose of this paper is to provide an efficient method for resource allocation in fog computing considering user mobility with the aim of increasing the acceptance rate of users' delay-sensitive requests and reducing the response time of requests. The proposed method predicts congestion in the fog node based on the mobility of users and the condition of its queue and, accordingly, decides whether to process the requests in the fog or to send them to the cloud. The experimental results indicated that the proposed method involves a lower response time and a higher acceptance rate than other existing methods. The results of the experiments show that the response time of the proposed method is reduced by 73, 77 and 10 percent compared to the three FCFS, Cloud-only and Delay priority algorithms, respectively. In the proposed algorithm, the request acceptance rate has increased by 44, 25 and 5 percent compared to the above three algorithms, respectively.
Keywords
Resource allocation
Fog computing
User mobility
Request Congestion
[1] A. Kishor1 and C. Chakarbarty, “Task Offloading in Fog Computing for Using Smart Ant Colony Optimization,” Wireless Personal Communications, Vol. 127, No.2, pp. 1683-1704, 2021.
[2] M. Kaur and R. Aron, “A systematic study of load balancing approaches in the fog computing environment,” The Journal of Supercomputing, Vol.77, No.8, pp. 9202-9247, 2021.
[3] S. Jain, S. Gupta, K.K. Sreelakshmi and J.J. Rodrigues, “Fog computing in enabling 5G-driven emerging technologies for development of sustainable smart city infrastructures,” Cluster Computing, Vol.25, pp. 1-44, 2022.
[4] S.K. Idrees and A.K. Idrees, “New fog computing enabled lossless EEG data compression scheme in IoT networks,” Journal of Ambient Intelligence and Humanized Computing, Vol.13, No.6, pp. 3257-3270, 2022.
[5] G. Javadzadeh and A.M. Rahmani, “Fog computing applications in smart cities: A systematic survey,” Wireless Networks, Vol.26, No.2, pp.1433-1457, 2020.
[6] L. Shooshtarian, D. Lan and A. Taherkordi, "A clustering-based approach to efficient resource allocation in fog computing," in International Symposium on Pervasive Systems, Algorithms and Networks, Springer, Cham, pp. 207-224, 2019.
[7] J. C.Guevara, and N.LS. da Fonseca, "Task scheduling in cloud-fog computing systems," Peer-to-Peer Networking and Applications, Vol.14, No.2, pp. 962-977, 2021.‏
[8] D. Baburao, T. Pavankumar, and C.S.R. Prabhu, "Load balancing in the fog nodes using particle swarm optimization-based enhanced dynamic resource allocation method," Applied Nanoscience, Vol.11, No.7, pp. 1-10, 2021.‏
[9] H. Wadhwa and R. Aron, "TRAM: Technique for resource allocation and management in fog computing environment," The Journal of Supercomputing, Vol.78, No.1, pp. 667-690, 2022.‏
[10] X.Q. Pham and E.N. Huh, “Towards Task scheduling in a Cloud Fog Computing System,” Network Operations and Management Symposium (APNOMS), Kanazawa, Japan, pp. 1-4, 2016.
[11] S. Bitam, S. Zeadally and A. Mellouk, “Fog Computing Job Scheduling Optimization Based on Bees Swarm,” Enterprise Information Systems, Vol.12, No.4, pp. 373-397, 2018.
[12] J. Fan, X. Wei, T. Wang, T. Lan and S. Subramaniam, “Deadline-Aware Task Scheduling in a Tiered IoT Infrastructure”. In 2017-2017 IEEE Global Communications Conference (GLOBECOM), Singapore, pp. 1-7, 2017.
[13] S.K. Mishra, D. Puthal, J.J.P.C. Rodrigues, B. Sahoo, and E. Dutkiewicz, “Sustainable Service Allocation Using a Metaheuristic Technique in a Fog Server for Industrial Applications,” IEEE Transactions on Industrial Informatics, Vol.14, No.10, pp. 4497-4506, 2018.
[14] S. Kabirzadeh, D. Rahbari, and M. Nickray, “A Hyper Heuristic Algorithm for Scheduling of Fog Networks”. In 2017 21st Conference of Open Innovations Association (FRUCT), Helsinki, Finland, pp. 148-155, 2017.
[15] S. Kabirzadeh, D. Rahbari, and M. Nickray, “A Security Aware Scheduling in Fog Computing by Hyper Heuristic Algorithm”. In 2017 3rd Iranian Conference on Intelligent Systems and Signal Processing (ICSPIS), Shahrood, Iran, pp. 87-92, 2017.
[16] T. Choudhari, M. Moh and T.S. Moh, “Prioritized Task Scheduling in Fog Computing”. In Proceedings of the ACMSE 2018 Conference (ACMSE), New York, NY, USA, pp.1-8, 2018.
[17] Y. Sun, T. Dang and J. Zhou, “User Scheduling and Cluster Formation in Fog Computing Based Radio Access Networks”. In 2016 IEEE International Conference on Ubiquitous Wireless Broadband (ICUWB), Nanjing, China, pp.1-4, 2016.
[18] V.B.C. Souza, W. Ramírez, X.M. Bruin, E.M. Tordera, G. Ren and G. Tashakor, “Handling Service Allocation in Combined Fog-Cloud Scenarios”. In 2016 IEEE International Conference on Communications (ICC), Kuala Lumpur, Malaysia, pp.1-5, 2016.
[19] H. Zhang, Y. Xiao, S. Bu, D. Niyato, F.R. Yu and Z. Han, “Computing Resource Allocation in Three-tier IoT Fog Networks: A Joint Optimization Approach Combining Stackelberg Game and Matching,” IEEE Internet of Things Journal, Vol.4, No. 5, pp. 1204-1215, 2017.
[20] R. Deng, R. Lu, C. Lai, T.H. Luan and H. Liang, “Optimal Workload Allocation in Fog-Cloud Computing Toward Balanced Delay and Power Consumption,” IEEE Internet of Things Journal, Vol.6, No.3, pp. 1171-1181, 2016.
[21] R. Deng, R. Lu, C. Lai and T.H. Luan, “Towards Power Consumption-Delay Tradeoff by Workload Allocation in Cloud-Fog Computing”. In 2015 IEEE International Conference on Communications (ICC), London, UK, pp. 3909-3914, 2015.
[22] L. Liu, D. Qi, N. Zhou, and Y. Wu, “A Task Scheduling Algorithm Based on Classification Mining in Fog Computing Environment,” Wireless Communications and Mobile Computing, Vol.2018, pp. 1-11, 2018.
[23] L.F. Bittencourt, J.D. Montes, R. Buyya, O.F. Rana and M. Parashar, “Mobility-Aware Application Scheduling in Fog Computing,” IEEE Cloud Computing, Vol.4, No.2, pp. 26-35, 2017.
[24] M. Verma, N. Bhardwaj, and A.K. Yadav, “Real Time Efficient Scheduling Algorithm for Load Balancing in Fog Computing Environment,” I.J. Information Technology and Computer Science, Vol.8, No.4, pp. 1-10, 2016.
[25] R. Mahmud, S. Pallewatta, M. Goudarzi and R.Buyya, “Ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments,” Journal of Systems and Software, Vol.190, pp. 1-17, 2022.
[26] Y. Jie, M. Li, C. Guo and L. Chen, “Game-theoretic online resource allocation scheme on fog computing for mobile multimedia users,” China Communications, Vol.16, No.3, pp.22-31, 2019.
[27] D. Wang, Z. Liu, X. Wang and Y. Lan, “Mobility-aware task offloading and migration schemes in fog computing networks,” IEEE Access, Vol.7, pp.43356-43368, 2019.
[28] سمیرا انصاری مقدم، سمیرا نوفرستی، مهری رجایی،"ارائه یک روش کارا برای تخصیص منابع در رایانش مه با در نظر گرفتن شرایط ازدحام درخواست‌ها"، نشریه مهندسی برق و مهندسی کامپیوتر ایران، جلد 19، شماره 1، 1400.
[29] W. Liu, C. Li, A. Zhen, Z. Zheng, Z. Zhang, and Y. Xiao, “Fog Computing Resource-Scheduling Strategy in IoT Based on Artificial Bee Colony Algorithm,” Electronics, Vol. 12, N. 7, pp. 1-24, 2023.
[30] O. Sharma, G. Rathee, C. A. Kerrache, and J Herrera-Tapia, “Two-Stage Optimal Task Scheduling for Smart Home Environment Using Fog Computing Infrastructures,” Applied Sciences, Vol. 13, N. 5, PP. 1-15, 2023.
[31] S. Iftikhar, M. M. M. Ahmad, S. Tuli, D. Chowdhury, M. Xu, S. Singh Gill, S. Uhlig, “HunterPlus: AI based energy-efficient task scheduling for cloud–fog computing environments,” Internet of Things, Vol.21, PP.1-17, 2023.
[32] R. Dharaniya, V. S. Satanya, S. Babu and S.A. Zaid, “Ai agents at different data centers to minimize the energy spending”. In International conference on advanced computing and communication systems (ICACCS), Coimbatore, India, pp.813-818, 2023.
[33] H. T. Dang and D.S Kim, “Dynamic Collaborative Task Offloading for Delay Minimization in the Heterogeneous Fog Computing Systems,” journal of communications and networks. Vol. 25, V. 2, 224-259, 2023
[34] H.R. Refaat and M.A. Mead, “DLBS: Decentralize Load-Balance Scheduling Algorithm for Real-Time IoT Services in Mist Computing,” International Journal of Advanced Computer Science and Applications(IJACSA), Vol.10, No.9, pp. 92-100, 2019.
[35] A. Khalid and M. Shahbaz, “Service Architecture Models for Fog Computing: A Remedy for Latency Issues in Data Access from Clouds,” Transactions on Internet and Information Systems, Vol.11, No.5, pp. 2310-2345, 2017.
[36] D. Rathod and C. Girish, “Load Balancing of Fog Computing Centers: Minimizing Response Time of High Priority Requests,” International Journal of Innovative Technology and Exploring Engineering, Vol.8, No.11, pp. 2713-2716, 2019.
[37] B. Dewulf, T. Neutens, M. Vanlommel and S. Logghe, “Examining Commuting Patterns Using Floating Car Data and Circular Statistics: Exploring the Use of New Methods and Visualizations to Study Travel Times,” Journal of Transport Geography, Vol.48, pp. 41-51, 2015.
[38] S.N. Srirama, K. Ramamohanarao, R. Buyya and M.R. Mahmud, “Quality of Experience (QoE)-aware Placement of Applications in Fog Computing Environments,” Journal of Parallel and Distributed Computing, Vol.132, pp. 190-203, 2018.
Journal of Soft Computing and Information Technology
Volume 12, Issue 2 - Serial Number 40
Summer
Summer 2023
Pages 16-31