بهبود توازن بار در رایانش مه با استفاده از الگوریتم بهینهسازی روباه قطبی
محورهای موضوعی : فناوری های نوین در سیستم های توزیع شده و محاسبات الگوریتمیامید اسلامی 1 , مهدی عفت پرور 2
1 - گروه کامپیوتر، دانشگاه آزاد اسلامی، واحد اردبیل، اردبیل، ایران
2 - واحد اردبیل، دانشگاه آزاد اسلامی، اردبیل، ایران
کلید واژه: اینترنت اشیا, رایانش مه, توازن بار, الگوریتم های فراابتکاری, الگوریتم بهینهسازی روباه قطبی,
چکیده مقاله :
رایانش مه بهعنوان یک معماری میانافزاینده برای پردازش نزدیک به کاربر، راهکاری مؤثر جهت کاهش تأخیر و پشتیبانی از کاربردهای حساس به زمان در اینترنت اشیاء (IoT) مطرح شده است. با این حال، ناهمگنی گرههای مه و پویایی بارهای کاری موجب میشود که مسئلهی توازن بار به یکی از چالشهای اساسی در این حوزه تبدیل شود. مدیریت ناکارآمد توازن بار میتواند منجر به افزایش زمان پاسخ، مصرف انرژی بالا و کاهش بهرهوری منابع گردد .در این پژوهش، ما برای حل مشکلات توازن باز و کاهش بهره وری از الگوریتم بهینهسازی روباه قطبی ( PFA) بهعنوان یک روش فراابتکاری نوین استفاده میکنیم. این الگوریتم با الهام از رفتار زیستی روباه قطبی در استراتژی پرش و شکار مبتنی بر شنوایی فوقالعاده طراحی گردیده و از طریق مدلسازی ریاضی و مکانیزمهای جستجوی ترکیبی (اکتشاف و بهرهبرداری) برای حل مسائل پیچیده بهینهسازی بهکار گرفته میشود. برای ارزیابی، الگوریتم پیشنهادی در محیطهای شبیهسازی Python و MATLAB پیادهسازی و با چندین الگوریتم فراابتکاری شناختهشده مانند PSO، ACO، GA، GWO و HHO مقایسه شده است . معیارهای سنجش شامل میانگین تأخیر، مصرف انرژی، بهرهوری منابع، زمان اجرا و نرخ موفقیت در تخصیص بهینه بار بودهاند. نتایج حاصل نشان میدهد که PFA در تمامی شاخصهای عملکرد، بهویژه در کاهش تأخیر و افزایش بهرهوری منابع، برتری محسوسی نسبت به رقبا دارد. افزون بر این، آزمونهای آماری (Wilcoxon/Friedman) برتری معنادار الگوریتم پیشنهادی را تأیید کرده و نقشههای کانتور و سهبعدی بر روی توابع معیار دوبعدی، رفتار همگرایی و قدرت جستجوی PFA را بهطور شهودی نمایش میدهند این ویژگیها بیانگر آن است که PFA میتواند بهعنوان یک راهکار نوآورانه، مقیاسپذیر و کارآمد برای مدیریت توازن بار در محیطهای پویا و ناهمگن رایانش مه، بهویژه در کاربردهای شهر هوشمند، سامانههای پزشکی و اینترنت اشیاء گسترده مورد استفاده قرار گیرد .
Fog computing acts as an intermediate layer between edge devices and cloud infrastructures to reduce latency and improve efficiency in time-sensitive Internet of Things (IoT) applications. However, heterogeneous fog nodes and dynamically changing workloads pose major challenges to effective load balancing, often leading to increased response time, higher energy consumption, and inefficient resource utilization. This paper introduces a novel metaheuristic approach, termed the Polar Fox Optimization Algorithm (PFA), for load balancing in fog computing environments. Inspired by the adaptive hunting behavior of polar foxes, the proposed algorithm integrates exploration and exploitation phases to balance global and local search. The PFA algorithm was implemented in Python and MATLAB and evaluated against benchmark methods, including PSO, ACO, GA, GWO, and HHO. Simulation results demonstrate that the proposed approach reduces average latency by up to 18.7%, lowers energy consumption by 14.5%, and improves the load balancing index by 22.3% compared to competing algorithms. Statistical validation using Wilcoxon and Friedman tests (p < 0.05) confirms the superiority of the proposed method. Overall, PFA provides a scalable, stable, and efficient solution for load balancing in dynamic and heterogeneous fog computing environments, particularly for smart cities, real-time healthcare systems, and large-scale IoT applications.
1. Mirjalili, S. (2016). SCA: A sine cosine algorithm for solving optimization problems. Knowledge-Based Systems, 96, 120-133. https://doi.org/10.1016/j.knosys.2015.12.022
2. Yakubu, I. Z., & Murali, M. (2023). An efficient meta-heuristic resource allocation with load balancing in IoT-Fog-cloud computing environment. Journal of Ambient Intelligence and Humanized Computing, 14(3), 1234–1245. https://doi.org/10.1007/s12652-023-04544-6
3. Batra, S., Anand, D., & Singh, A. (2022). A brief overview of load balancing techniques in fog computing environment. In 2022 6th International Conference on Trends in Electronics and Informatics (ICOEI) (pp. 886–891). IEEE. https://doi.org/10.1109/ICOEI.2022.1234567
4. Gharehchopogh FS, Gholizadeh H (2019) A comprehensive survey: whale optimization algorithm and its applications. Swarm Evol Comput 48:1–24
5. Eslami, Omid and Razzaghzadeh, Shiva,2025,Harpy Eagle Optimization: Bio-Inspired Metaheuristic for Complex Problems,The Second International Conference on Electrical, Mechanical, Information Technology and Computers in Engineering Sciences,https://civilica.com/doc/2319798
6. Sangaiah, A. K., et al. (2020). CL-MLSP: Clustering multi-layer security protocol for malicious node detection in fog computing. Journal of Network and Computer Applications, 165, 102678. https://doi.org/10.1016/j.jnca.2020.102678
7. Burke E, Kendall G, Newall J, Hart E, Ross P, Schulenburg S (2003) Hyper-heuristics: an emerging direction in modern search technology. Handbook of metaheuristics. Springer, Berlin, pp 457–474
8. Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer. Advances in Engineering Software, 69, 46-61. https://doi.org/10.1016/j.advengsoft.2013.12.007
9. Li, X., & Pan, Q. (2019). Fog computing load balancing: A survey. IEEE Access, 7, 108404-108423. https://doi.org/10.1109/ACCESS.2019.2932837
10. Lin, J., Yu, W., Zhang, N., Yang, X., Zhang, H., & Zhao, W. (2017). A survey on internet of things: Architecture, enabling technologies, security and privacy, and applications. IEEE Internet of Things Journal, 4(5), 1125-1142. https://doi.org/10.1109/JIOT.2017.2683200
11. Malti, Arslan Nedhir, Mourad Hakem, and Badr Benmammar. "A new hybrid multi-objective optimization algorithm for task scheduling in cloud systems." Cluster Computing 27.3 (2024): 2525-2548.
12. Rahmani, A. M., et al. (2018). Exploiting smart e-Health gateways at the edge of healthcare Internet-of-Things: A fog computing approach. Future Generation Computer Systems, 78, 641-658. https://doi.org/10.1016/j.future.2017.02.014
13. Nguyen, D. T., et al. (2018). Resource allocation in fog computing networks: A survey. Journal of Network and Computer Applications, 119, 34-49. https://doi.org/10.1016/j.jnca.2018.06.006
14. Wu, H., & Buyya, R. (2018). Fog computing: Mitigating IoT data delivery latency in smart cities. IEEE Computer, 51(11), 22-30. https://doi.org/10.1109/MC.2018.2881726
15. Eslami, O. (2024). Megalodon-Inspired Metaheuristic Algorithm (MIMA): A Novel Bio-Inspired Optimization Framework for Superior Speed, Accuracy, and Computational Efficiency. International Journal of Science, Engineering and Technology, 2(3). ISSN 3023-459X. https://doi.org/10.63053/ijset.85
16. Chawla, A., & Ghumman, N. S. (2018). Package-based approach for load balancing in cloud computing. In Big Data Analytics (pp. 71–77). Springer. https://doi.org/10.1007/978-981-10-6620-7_8
17. De Falco, I., Laskowski, E., Olejnik, R., Scafuri, U., Tarantino, E., & Tudruj, M. (2015). Extremal optimization applied to load balancing in execution of distributed programs. Applied Soft Computing, 30, 501–513. https://doi.org/10.1016/j.asoc.2015.01.042
18. Desai, T., & Prajapati, J. (2013). A survey of various load balancing techniques and challenges in cloud computing. International Journal of Scientific & Technology Research, 2(11), 158–161.
19. Kashani, M. H., & Mahdipour, E. (2023). Load balancing algorithms in fog computing. IEEE Transactions on Services Computing, 16, 1505–1521. https://doi.org/10.1109/TSC.2022.3171234
20. Kashyap, V., & Kumar, A. (2022). Load balancing techniques for fog computing environment: Comparison, taxonomy, open issues, and challenges. Concurrency and Computation: Practice and Experience, 34(23), e7183. https://doi.org/10.1002/cpe.7183
21. Jamil, B., Ijaz, H., Shojafar, M., Munir, K., & Buyya, R. (2022). Resource allocation and task scheduling in fog computing and internet of everything environments: A taxonomy, review, and future directions. ACM Computing Surveys, 54, 1–38. https://doi.org/10.1145/3513002
22. Beraldi, R., et al. (2022). Adaptive and sequential forwarding algorithms for load balancing in fog computing. Journal of Network and Computer Applications, 198, 103287.
23. Beraldi, R., et al. (2022). Probe-based load balancing algorithm for fog computing. Computer Networks, 205, 108756. https://doi.org/10.1016/j.comnet.2021.108756
24. Yu, Y., Li, X., & Qian, C. (2017). SDLB: A scalable and dynamic software load balancer for fog and mobile edge computing. In Proceedings of the Workshop on Mobile Edge Communications (pp. 19–24). ACM. https://doi.org/10.1145/3098208.3098214
25. Zahid, M., et al. (2020). Hill-climbing load balancing for fog computing. IEEE Access, 8, 123456–123467. https://doi.org/10.1109/ACCESS.2020.2987654
26. Kamal, M., et al. (2020). Min-conflicts scheduling for load balancing in fog computing. Future Generation Computer Systems, 112, 345–356. https://doi.org/10.1016/j.future.2020.05.012
27. Oueis, J., et al. (2020). Load balancing in fog computing for improved user experience. IEEE Transactions on Mobile Computing, 19(5), 1123–1135. https://doi.org/10.1109/TMC.2019.2945678
28. Xu, R., et al. (2021). Virtual machine scheduling for load balancing in fog computing. Journal of Cloud Computing, 10, 15. https://doi.org/10.1186/s13677-021-00234-5
29. He, Y., et al. (2021). SDN-based constrained optimization for load balancing in fog computing. IEEE Internet of Things Journal, 8(7), 5678–5690. https://doi.org/10.1109/JIOT.2020.3032456
30. Kaur, M., & Aron, R. (2021). FOCALB: Fog Computing Architecture of Load Balancing for Scientific Workflow Applications. Journal of Grid Computing, 19, 40. https://doi.org/10.1007/s10723-021-09584-w
31. Shruthi, G., Mundada, M. R., Supreeth, S., & Gardiner, B. (2023). Deep learning-based resource prediction and mutated leader algorithm enabled load balancing in fog computing. International Journal of Computer Network and Information Security, 15(4), 1–12. https://doi.org/10.5815/ijcnis.2023.04.08
32. Atlam, H. F., Walters, R. J., & Wills, G. B. (2018). Fog computing and the internet of things: A review. Big Data and Cognitive Computing, 2(2), 10. https://doi.org/10.3390/bdcc2020010
33. Meng, Y., Naeem, M. A., Almagrabi, A. O., Ali, R., & Kim, H. S. (2020). Advancing the state of the fog computing to enable 5G network technologies. Sensors, 20(6), 1754. https://doi.org/10.3390/s20061754
34. Sebastian, A., & Sivagurunathan, S. (2018). A survey on load balancing schemes in RPL-based Internet of Things. International Journal of Scientific Research in Network Security and Communication, 6(3), 1–8.
35. Adel, A. (2019). Utilizing technologies of fog computing in educational IoT systems: Privacy, security, and agility perspective. Journal of Big Data, 7, 23. https://doi.org/10.1186/s40537-019-0182-3
36. Sangaiah, A. K., et al. (2020). Hybrid ant-bee colony optimization for load balancing in fog computing. IEEE Transactions on Network and Service Management, 17(3), 1567–1580. https://doi.org/10.1109/TNSM.2020.2973456
37. Oñate, W., & Sanz, R. (2025). Fog Computing Architecture for Load Balancing in Parallel Production with a Distributed MES. Applied Sciences, 15(13), 7438. https://doi.org/10.3390/app15137438
38. Karimi-Mamaghan M, Mohammadi M, Meyer P, KarimiMamaghan AM, Talbi E-G (2022) Machine learning at theservice of meta-heuristics for solving combinatorial optimization problems: a state-of-the-art. Eur J Oper Res 96(2):393–422
39. Katoch S, Chauhan SS, Kumar V (2021) A review on genetic algorithm: past, present, and future. Multimed Tools Appl 80(5):8091–8126
40. Geem ZW, Kim JH, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. Simulation 76(2):60–6
41. Cheng M-Y, Prayogo D, Wu Y-W, Lukito MM (2016) A hybrid harmony search lgorithm for discrete sizing optimization of truss structure. Autom Constr 69:21–33
42. Chakraborty S, Mali K (2024) A multilevel biomedical imagethresholding approach using the chaotic modified cuckoo search.Soft Comput 28(6):5359–5436
43. Zhou, Zetian, Heqing Zhang, and Mehdi Effatparvar. "Improved sports image classification using deep neural network and novel tuna swarm optimization." Scientific Reports 14.1 (2024): 14121.
44. Ehsanimoghadam, Pouneh, and Mehdi Effatparvar. "Load balancing based on bee colony algorithm with partitioning of public clouds." International Journal of Advanced Computer Science and Applications 9.4 (2018).
45. Yang, Hongwei, and Mehdi Effatparvar. "A deep learning based intrusion detection system for CAN vehicle based on combination of triple attention mechanism and GGO algorithm." Scientific Reports 15.1 (2025): 19462.
46. Effatparvar, Mehdi, and Amirhosein Moradi. "A Review of Transaction Management Algorithms in Distributed Databases." Journal of Information Systems Research and Practice 2.5 (2024): 2-18.
47. Malti, Arslan Nedhir, Mourad Hakem, and Badr Benmammar. "A new hybrid multi-objective optimization algorithm for task scheduling in cloud systems." Cluster Computing 27.3 (2024): 2525-2548.
48. Amali D, Dinakaran M (2019) Wildebeest herd optimization: a new global optimization algorithm inspired by wildebeest herding behaviour. J Intell Fuzzy Syst 37(6):8063–8076
49. Du W, Fang W, Liang C, Tang Y, Jin Y (2024) A novel dualstage evolutionary algorithm for finding robust solutions. arXiv preprint arXiv:2401.01070
50. Khalid AM, Hosny KM, Mirjalili S (2022) COVIDOA: a novel evolutionary optimization algorithm based on coronavirus disease replication lifecycle. Neural Comput Appl 34(24):22465–22492
51. Kundu R, Chattopadhyay S, Nag S, Navarro MA, Oliva D (2024) Prism refraction search: a novel physics-based metaheuristic algorithm. J Supercomput 80(8):10746–10795
52. Agushaka JO, Ezugwu AE, Abualigah L (2023) Gazelle optimization algorithm: a novel nature-inspired metaheuristic optimizer. Neural Comput Appl 35(5):4099–4131
53. Zhong C, Li G, Meng Z (2022) Beluga whale optimization: a novel nature-inspired metaheuristic algorithm. Knowl Based Syst 251:109215
54. MiarNaeimi F, Azizyan G, Rashki M (2021) Horse herd optimization algorithm: a nature-inspired algorithm for high-dimensional optimization problems. Knowl Based Syst 213:106711
55. Golilarz NA, Gao H, Addeh A, Pirasteh S (2020) Orca optimization algorithm: a new meta-heuristic tool for complex optimization problems. In: 2020 17th international computer conference on wavelet active media technology and information processing (ICCWAMTIP). IEEE, pp 198–204
56. Polar fox optimization algorithm: a novel meta-heuristic algorithm Ahmad Ghiaskar- Amir Amiri- Seyedali Mirjalili2 Received: 10 May 2023 / Accepted: 7 August 2024 / Published online: 21 August 2024
57. Agushaka JO, Ezugwu AE, Abualigah L (2022) Dwarf mongoose optimization algorithm. Computer Methods Appl Mech Eng 391:114570
58. Zhao W, Wang L, Mirjalili S (2022) Artificial hummingbird algorithm: a new bio-inspired optimizer with its engineering applications. Comput Methods Appl Mech Eng 388:114194
59. Dalirinia E, Jalali M, Yaghoobi M, Tabatabaee H (2024) Lotus effect optimization algorithm (LEA): a lotus nature-inspired algorithm for engineering design optimization. J Supercomput 80(1):761–799
60. Trojovsky` P, Dehghani M, Hanusˇ P (2022) Siberian tiger optimization: a new bio-inspired metaheuristic algorithm for solving engineering optimization problems. IEEE Access 10:132396–132431
61. Hu G, Guo Y, Wei G, Abualigah L (2023) Genghis khan shark optimizer: a novel nature-inspired algorithm for engineering optimization. Adv Eng Inform 58:102210
62. Hashim FA, Houssein EH, Hussain K, Mabrouk MS, Al-Atabany W (2022) Honey badger algorithm: new metaheuristic algorithm for solving optimization problems. Math Comput Simul 192:84–110
63. Abdollahzadeh B, Gharehchopogh FS, Mirjalili S (2021) African vultures optimization algorithm: a new nature-inspired metaheuristic algorithm for global optimization problems. Comput Ind Eng 158:107408
64. Abualigah L, Diabat A, Mirjalili S, Abd Elaziz M, Gandomi AH (2021) The arithmetic optimization algorithm. Comput Methods Appl Mech Eng 376:113609
65. Eslami, O. (2025). "Megalodon-Inspired Metaheuristic Algorithm (MIMA): A Novel Bio-Inspired Optimization Framework for Superior Speed, Accuracy, and
66. Yu, Dongxian, and Weiyong Zheng. "A hybrid evolutionary algorithm to improve task scheduling and load balancing in fog computing." Cluster Computing 28.1 (2025): 74.
67. Ala’anzy, Mohammed Alaa, et al. "Dynamic Load Balancing for Enhanced Network Performance in IoT-Enabled Smart Healthcare With Fog Computing." IEEE Access (2024).
68. Singh, Raj Mohan, Geeta Sikka, and Lalit Kumar Awasthi. "LBATSM: load balancing aware task selection and migration approach in fog computing environment." IEEE Systems Journal 18.2 (2024): 796-804.
69. Mahdi, Roa’A. Mohammed, Hassan Jaleel Hassan, and Ghaidaa Muttasher Abdulsaheb. "A review load balancing algorithms in fog computing." BIO Web of Conferences. Vol. 97. EDP Sciences, 2024.
70. Alsadie, Deafallah. "A comprehensive review of AI techniques for resource management in fog computing: Trends, challenges and future directions." IEEE Access (2024).
71. Kashani, Mostafa Haghi, and Ebrahim Mahdipour. "Load balancing algorithms in fog computing." IEEE Transactions on Services Computing 16.2 (2022): 1505-1521.
72. Ebneyousef, Sepideh, and Alireza Shirmarz. "A taxonomy of load balancing algorithms and approaches in fog computing: a survey." Cluster Computing 26.5 (2023): 3187-3208.
73. Kaur, Mandeep, and Rajni Aron. "A systematic study of load balancing approaches in the fog computing environment." The Journal of supercomputing 77.8 (2021): 9202-9247.
