An Optimized Method for Outsourcing and Computational Offloading In Resources Allocation to IoT Users In Fog computing

Document Type : Research Paper

Authors

1 Department of Computer Engineering, Kerman Branch, Islamic Azad University, Kerman, Iran.

2 Department of Computer Engineering, Shahid Bahonar University of Kerman, Kerman, Iran.

Abstract

Fog computing is a method for improving cloud computations performance attempts to expand the Internet of things processes and distribute cloud services in the network edge. This paper proposes a real-time outsourcing and offloading mechanism to optimize the cache and CPU consumption in resource allocation to IoT users in a fog-based processing environment. Based on this mechanism, the computations that require heavy processing are moved to the network edge, and computations with lower processing needs are processed inside user devices. According to the simulation results, the average users' average service latency in the proposed method SPA-(Offloading) for 200 users has been improved in the range of 0.8 to 0.6. In addition, the profits of cloud and fog service providers for 220 users are higher than other methods. Also, the average system cost performance was evaluated, which is better than the other methods. The results show that this mechanism improves cache consumption, processing time, and optimal resource allocation to IoT users.

Keywords

References

1- S. M. Mirrezaei “Improving the Efficiency of Wireless Sensor Networks Using Fountain Codes,” Journal of Communication Engineering, vol. 9, no. 1, pp. 168-183, Jan. 2020.
2- J. Tavakoli  L. Amini , N. Moghim and F. Pasandideh  “A Fuzzy Based Energy Efficient Clustering  Routing Protocol in Underwater Sensor Networks,” Journal of Communication Engineering, vol. 9, no.1, pp. 154-167, Jan. 2020.
3- J. Ren, D. Zhang, S. He, Y. Zhang and T. Li, “A Survey on End-Edge-Cloud Orchestrated Network Computing Paradigms: Transparent Computing, Mobile Edge Computing, Fog Computing, and Cloudlet,” ACM Computing Surveys (CSUR), vol. 52, no. 6, pp. 1–36, Jan. 2019.
4- S. P. Singh, A. Nayyar, R. Kumar, and A. Sharma, “Fog computing: from architecture to edge computing and big data processing,” The Journal of  Super Computing, vol. 75, no. 4, pp. 2070–2105, Nov. 2019.
5- J. Fei and M. Xiaoping, “Fog computing perception mechanism based on throughput rate constraint in intelligent Internet of Things,” Personal and Ubiquitous Computing, vol. 23, no. 3-4, pp. 563–571, July 2019.
6- J. P. Rajan, S. E. Rajan, R. J. Martis, and B. K. Panigrahi, “Fog Computing Employed Computer-Aided Cancer Classification System Using Deep Neural Network in the Internet of Things Based Healthcare System,” Journal of Medical Systems,  vol. 44, no. 34, Dec. 2020.
7- J. Chen, H. Xing, X. Lin, and S. Bi, “Joint Cache Placement and Bandwidth Allocation for FDMA-based Mobile Edge Computing Systems,” IEEE International Conference on Communications (ICC), July 2020.
8- X. Huang , Y. Cui , Q. Chen, and J. Zhang, “Joint Task Offloading and QoS-aware Resource Allocation in Fog-enabled Internet of Things Networks,” IEEE Internet of Things Journal, vol.7, no. 8, pp. 7194-7206, Aug. 2020.
9- P. Cai, F. Yang, J. Wang, X. Wu, Y. Yang, and X. Luo “JOTE: Joint Offloading of Tasks and Energy in Fog-Enabled IoT Networks,” IEEE 90th Vehicular Technology Conference, Nov. 2019.
10- N Kiran, C Pan, S Wang, and C Yin, “Joint Resource Allocation and Computation Offloading in Mobile Edge Computing for SDN based Wireless Networks,” Journal of Communications and Networks, vol. 22, no.1 pp. 1-11, Feb. 2020.
11- W. Wen, Y. Cui, T. Q. S. Quek, F. C. Zheng, and S. Jin, “Joint Optimal Software Caching, Computation Offloading and Communications Resource Allocation for Mobile Edge Computing,” IEEE Trans. Vehicular Technology, arXiv:2005.02627v1, pp. 1-15, May 2020.
12- X. Gao, Xi Huang, S. Bian, Z. Shao, and Y. Yang, “PORA: Predictive Offloading and Resource Allocation in Dynamic Fog Computing Systems,” IEEE Internet of Things Journal, vol. 7, no. 1, pp. 72-87, Jan. 2020.
13- Q. Li, J. Zhao, Y. Gong, and Q. Zhang, “Energy-Efficient Computation Offloading and Resource Allocation in Fog Computing for Internet of Everything,” China Communications, vol. 16, no. 3, pp. 32-41, Mar. 2019.
 14- F.M.Talaat., S. H. Ali, A.I. Saleh, and H.A. Ali, ”Effective Load Balancing Strategy (ELBS) for Real-Time Fog Computing Environment Using Fuzzy and Probabilistic Neural Networks,” Journal of Network and Systems Management, vol. 27, pp.883-929, Feb.  2019.
 15- E. Schleicher, K. Graffi, and A. Rabay, ”Fog Computing with P2P: Enhancing Fog Computing Bandwidth for IoT Scenarios,” International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (Green Com) and IEEE Cyber, Physical and Social Computing (CPS Com) and IEEE Smart Data (Smart Data), 14-17 July  2019.
16- Z. Chang ,L. Liu, X. Guo, and Q. Sheng “Dynamic Resource Allocation and Computation Offloading for IoT Fog Computing System,” IEEE Trans. Industrial Informatics, vol. 17, no. 5, pp. 3348-3357, May 2021.
17- G. Jia, G. Han, H. Wang and F. Wang, “Cost aware cache replacement policy in shared last-level cache for hybrid memory based fog computing,” Enterprise Information Systems, vol. 12, no. 4, pp. 435-451, Feb. 2017. 
18- A. Khalili and S. Akhlaghi, “Power Control and Scheduling For Low SNR Region in the Uplink of Two Cell Networks,” Journal of Communication Engineering, vol.7, no.1, pp. 34-48, Jan. 2018.
19- K. Akherfi, M. Gerndt, and H. Harroud, “Mobile cloud computing for computation offloading: Issues and challenges,” Applied Computing and Informatics, vol. 14, no. 1, pp. 1-16, Jan. 2018.
20- R. M. Shukla and A. Muni, “An Efficient Computation Offloading Architecture for the Internet of Things (IoT) Devices,” 2017 14th IEEE Annual Consumer Communications & Networking Conference (CCNC), July 2017.
21- H. Wei, H. Luo, Y. Sun  and M. S. Obaidat, Life Fellow, “Cache-Aware Computation Offloading in IoT Systems,” IEEE Systems Journal, vol. 14, no. 1, pp. 61-72, Mar. 2020.
22- Y. Gu, Z. Chang, M. Pan, L. Song, and Z. Han, “Joint Radio and Computational Resource Allocation in IoT Fog Computing,” IEEE Trans. Vehicular Technology, vol. 67, no. 8, pp.7475-7484, Aug. 2018.
 

Files

Share

How to cite

Statistics