Increase the Efficiency of the Offloading Algorithm in Fog Computing by Particle Swarm Optimization Algorithm

Subject Areas : Metaphorical algorithms

Seyed Ebrahim Dashti ¹ ( Department of Computer Engineering- Jahrom Branch, Islamic Azad University, Jahrom, Iran )
Hoasain Zare ² ( Department of Computer Engineering- Zahedshar, Branch, Islamic Azad University, Zahedshar, Iran )

Received: 2022-07-31 Accepted : 2022-11-22 Published : 2022-11-22

Keywords: particle swarm optimization algorithm, Evolutionary Algorithm, Fog computing, Offloading, cloud processing,

Abstract :

Edge computing is a computing paradigm that extends cloud services to devices at the edge. This processing model refers to technologies that allow computing and storage to be performed on devices at the edge of the network. In this architecture, computing and storage operations take place close to objects and data sources. In order to reduce latency and network traffic between end devices and cloud centers, groups at the edge have processing capabilities, perform a large number of processing and computing tasks, including data processing, temporary storage, device management, decision making, and privacy protection. Since the number of edge devices is large, there must be a mechanism to select these tasks and offload them to the cloud. The problem to be decided is that which one of the available edge devices should be selected for unloading and then unloaded. This problem is classified as one of the hard non-polynomial problems and by using deterministic algorithms simply and in polynomial time, it is not possible to find a suitable and efficient solution for it found.

References:

[1] M. Keshavarznejad, M.H. Rezvani, S. Adabi, "Delay-aware optimization of energy consumption for task offloading in fog environments using metaheuristic algorithms", Cluster Computing, vol. 24, no. 3, pp. 1825-1853, Sept. 2021 (doi: 10.1007/s10586-020-03230-y).

[2] A. Shakarami, M. Ghobaei-Arani, M. Masdari, M. Hosseinzadeh, "A survey on the computation offloading approaches in mobile edge/cloud computing environment: A stochastic-based perspective", Journal of Grid Computing, vol. 18, no. 4, pp. 639–671, Dec. 2020 (doi: 10.1007/s10723-020-09530-2).

[3] A.M.A. Hamdi, F.K. Hussain, O.K. Hussain. "Task offloading in vehicular fog computing: State-of-the-art and open issues", Future Generation Computer Systems, vol. 133, pp. 201-212, Aug. 2022 (doi: 10.1016/­j.f­ut­­ure.2022.03.019).

[4] A. Banerjee, U.C. Gupta. "Reducing congestion in bulk-service finite-buffer queueing system using batch-size-dependent service", Performance Evaluation, vol. 69, no. 1, pp. 53-70, Jan. 2012 (doi: 10.101­6/j.p­eva.2­011.09.002).

[5] A. Shakarami, M. Ghobaei-Arani, M. Masdari, M. Hosseinzadeh, "A survey on the computation offloading approaches in mobile edge/cloud computing environment: a stochastic-based perspective", Journal of Grid Computing, vol. 18, no. 4, pp. 639-671, Aug. 2020 (doi: 10.1007/s10723-020-09530-2).

[6] S. AlShathri, S.A. Chelloug, D.S.M. Hassan, "Parallel meta-heuristics for solving dynamic offloading in fog computing", Mathematics, vol. 10, no. 8, Article Number: 1258, April 2022 (doi: 10.3390/math10081258).

[7] M. Zhang, L. Liu, S. Liu. "Genetic algorithm based QoS-aware service composition in multi-cloud", Proceeding of the IEEE/CIC, Hangzhou, China, Oct. 2015 (doi: 10.1109/CIC.2015.23).

[8] M. Chen, M. Dong, B. Liang, "Resource sharing of a computing access point for multi-user mobile cloud offloading with delay constraints", IEEE Trans. on Mobile Computing, vol. 17, no. 12, pp. 2868-2881, March 2018 (doi: 10.1109/TMC.2018.2815533).

[9] H. Boostanimehr, K. Vijay, "Joint downlink and uplink aware cell association in HetNets with QoS prov­isi­on­ing", IEEE Trans. on Wireless Communications, vol. 14, no. 10, pp. 5388-5401, Oct. 2015 (doi: 10.48550/arXiv.2110.11121).

[10] Q. Yao, T. QS Quek, A. Huang, H. Shan, "Joint downlink and uplink energy minimization in WET-enabled networks", IEEE Trans. on Wireless Communications, vol. 16, no. 10, pp. 6751-6765, Oct. 2017 (doi: 10.1155/2018/7906957).

[11] C. Wang, F. Richard Yu, C. Liang, Q. Chen, L. Tang, "Joint computation offloading and interference management in wireless cellular networks with mobile edge computing", IEEE Trans. on Vehicular Technology, vol. 66, no. 8, pp. 7432-7445, Aug. 2017 (doi: 10.1109/GLOCOM.2018.8647593).

[12] Z. Dong, Y. Liu, H. Zhou, X. Xiao, Y. Gu, L. Zhang, C. Liu, "An energy-efficient offloading framework with predictable temporal correctness", Proceedings of the Second ACM/IEEE Symposium on Edge Computing, pp. 1-12, 2017 (doi: 10.1145/3132211.3134448).

[13] G. Klas, "Edge computing and the role of cellular networks", Computer, vol. 50, no. 10, pp. 40-49, Oct. 2017 (doi: 10.1109/MC.2017.3641649).

[14] B. Huang, Z. Li, P. Tang, S. Wang, J. Zhao, H. Hu, W. Li, V. Chang. "Security modeling and efficient computation offloading for service workflow in mobile edge computing", Future Generation Computer Systems, vol. 97, pp. 755-774, Aug. 2019 (doi: 10.1016/j.future.2019.03.011).

[15] S.R. Nabavi, N. Osati-Eraghi, J. Akbari-Torkestani, "Wireless sensor networks routing using clust­eri­ng based on multi-objective particle swarm optimization algorithm", Journal of Intelligent Procedures in Electrical Technology, vol. 12, no. 47, pp. 29-47, Dec. 2021 (in Persian) (dor: 20.1001.1.2­3223­871.140­0.1­2.3.3.3).

[16] M. Momeny, S. Gharravi, F. Hourali, “Reducing the impact of SYN flood attacks by improving the accu­ra­cy of the PSO algorithm by adaptive effective filters”, Journal of Intelligent Procedures in Electrical Technology, vol. 10, no. 37, pp. 51-57, May 2019 (in Persian) (dor: 20.1001.1.23223871.1398.10.37.6.0).

[17] Q. Wang, Y. Mao, Y. Wang, L. Wang. "Computation tasks offloading scheme based on multi-cloudlet collaboration for edge computing", Proceeding of the IEEE/CBD, pp. 339-344, Suzhou, China, Sept. 2019 (doi: 10.1109/CBD.2019.00067).

[18] D. Jun, E. Gelenbe, C. Jiang, H. Zhang, Y. Ren. "Contract design for traffic offloading and resource allocation in heterogeneous ultra-dense networks", IEEE Journal on Selected Areas in Communications, vol. 35, no. 11, pp. 2457-2467, Nov. 2017 (doi: 10.1109/JSAC.2017.2760459).

[19] G. Mardanian, N. Behzadfar, “A new method for detection of breast cancer in mammography images using a firefly algorithm”, Journal of Intelligent Procedures in Electrical Technology, vol. 10, no. 40, pp. 23-32, March 2020 (in Persian) (dor: 20.1001.1.23223871.1398.10.40.3.3).

[20] L. Abualigah, M. Shehab, M. Alshinwan, S.A. Mirjalili, M.A. Elaziz, "Ant lion optimizer: A comprehensive survey of its variants and applications", Archives of Computational Methods in Engineering, vol. 28, pp. 1397–1416, May 2021 (doi: 10.1007/s11831-020-09420-6).

[21] Z. Song, C. Yu, "Investor sentiment indices based on k-step PLS algorithm: A group of powerful predictors of stock market returns", International Review of Financial Analysis, vol. 83, Article Number: 102321, Oct. 2022 (doi: 10.1016/j.irfa.2022.102321).