آمار

تعداد نشریات8
تعداد شماره‌ها417
تعداد مقالات5,494
تعداد مشاهده مقاله6,136,551
تعداد دریافت فایل اصل مقاله5,380,674
Ghafari, Reyhane, mansouri, najme. (1403). Efficient scheduling algorithm for optimizing system load in fog computing environment: A fuzzy reinforcement learning mechanism. نشریه مهندسی عمران امیرکبیر, 56(1), 33-54. doi: 10.22060/miscj.2024.23205.5360
Reyhane Ghafari; najme mansouri. "Efficient scheduling algorithm for optimizing system load in fog computing environment: A fuzzy reinforcement learning mechanism". نشریه مهندسی عمران امیرکبیر, 56, 1, 1403, 33-54. doi: 10.22060/miscj.2024.23205.5360
Ghafari, Reyhane, mansouri, najme. (1403). 'Efficient scheduling algorithm for optimizing system load in fog computing environment: A fuzzy reinforcement learning mechanism', نشریه مهندسی عمران امیرکبیر, 56(1), pp. 33-54. doi: 10.22060/miscj.2024.23205.5360
Ghafari, Reyhane, mansouri, najme. Efficient scheduling algorithm for optimizing system load in fog computing environment: A fuzzy reinforcement learning mechanism. نشریه مهندسی عمران امیرکبیر, 1403; 56(1): 33-54. doi: 10.22060/miscj.2024.23205.5360

Efficient scheduling algorithm for optimizing system load in fog computing environment: A fuzzy reinforcement learning mechanism

AUT Journal of Modeling and Simulation
مقاله 3، دوره 56، شماره 1، 2024، صفحه 33-54    اصل مقاله (1.53 M)
نوع مقاله: Research Article
شناسه دیجیتال (DOI): 10.22060/miscj.2024.23205.5360
نویسندگان
Reyhane Ghafari؛ najme mansouri*
Department of Computer Science, Shahid Bahonar University of Kerman, Kerman, Iran
چکیده
New technologies have emerged over the last few years, such as IoT and fog computing. IoT devices and the enormous amounts of data generated every minute have led to the vast growth of the Internet of Things (IoT). In order to meet the term "Data Never Sleeps", some IoT applications require real-time services and low bit latency. To provide quick processing, storage, and services, Cisco proposed fog computing as an extension of cloud computing. The traditional methods are not capable of addressing the complex scheduling scenarios of fog computing. In this paper, we introduce a novel Fuzzy Reinforcement Learning Scheduling algorithm (FRLS) that enhances schedule accuracy in dynamic computing environments. To optimize task scheduling, the FRLS algorithm integrates fuzzy logic with reinforcement learning. To prioritize critical tasks, fuzzy logic handles uncertainty and prioritizes tasks according to deadlines, sizes, and file sizes. Then, reinforcement learning schedules the prioritized tasks, continually adjusting to dynamic conditions to ensure the best resource allocation. In addition to improving overall system performance, this combination provides a robust framework that can address the complexity and variability of fog computing environments. FRLS is designed to minimize response time while adhering to resource and deadline constraints in fog-based applications. A comparison of FRLS with existing algorithms shows that it significantly improves load balancing, deadline satisfaction, response time, and waiting time. Combining reinforcement learning and fuzzy logic leads to an efficient scheduling solution. In addition, FRLS outperforms non-prioritized algorithms.
کلیدواژه‌ها
Fog Computing؛ Reinforcement Learning (RL)؛ Fuzzy Logic؛ Scheduling؛ Internet of Things (IoT)
مراجع
  1. Salaht, F. A., Desprez, F., and Lebre, A. “An overview of service placement problem in fog and edge computing.” ACM Computing Surveys (CSUR), Vol. 53, No. 3, (2020), 1–35.
  2. Archana, R. “Multilevel scheduling mechanism for a stochastic fog computing environment using the HIRO model and RNN.” Sustainable Computing: Informatics and Systems, Vol. 39, , (2023), 100887.
  3. Bonomi, F., Milito, R., Zhu, J., and Addepalli, S. “Fog computing and its role in the internet of things.” In Proceedings of the first edition of the MCC workshop on Mobile cloud computing (pp. 13–16).
  4. Fahimullah, M., Ahvar, S., Agarwal, M., and Trocan, M. “Machine learning-based solutions for resource management in fog computing.” Multimedia Tools and Applications, Vol. 83, No. 8, (2024), 23019–23045.
  5. Rahimikhanghah, A., Tajkey, M., Rezazadeh, B., and Rahmani, A. M. “Resource scheduling methods in cloud and fog computing environments: a systematic literature review.” Cluster Computing, (2022), 1–35.
  6. Ghafari, R., and Mansouri, N. “A novel energy-based task scheduling in fog computing environment: an improved artificial rabbits optimization approach.” Cluster Computing, (2024), 1–46.
  7. Iftikhar, S., Gill, S. S., Song, C., Xu, M., Aslanpour, M. S., Toosi, A. N., Du, J., Wu, H., Ghosh, S., and Chowdhury, D. “AI-based fog and edge computing: A systematic review, taxonomy and future directions.” Internet of Things, Vol. 21, , (2023), 100674.
  8. Zabihi, Z., Eftekhari Moghadam, A. M., and Rezvani, M. H. “Reinforcement Learning Methods for Computation Offloading: A Systematic Review.” ACM Computing Surveys, Vol. 56, No. 1, (2023), 1–41.
  9. Gasmi, R., Hammoudi, S., Lamri, M., and Harous, S. “Recent Reinforcement Learning and Blockchain Based Security Solutions for Internet of Things: Survey.” Wireless Personal Communications, Vol. 132, No. 2, (2023), 1307–1345.
  10. Abdullah, L. “Fuzzy multi criteria decision making and its applications: a brief review of category.” Procedia-Social and Behavioral Sciences, Vol. 97, , (2013), 131–136.
  11. Jalali Khalil Abadi, Z., and Mansouri, N. “A comprehensive survey on scheduling algorithms using fuzzy systems in distributed environments.” Artificial Intelligence Review, Vol. 57, No. 1, (2024), 4. https://doi.org/10.1007/s10462-023-10632-y
  12. Al-Araji, Z. J., Ahmad, S. S. S., Kausar, N., Anis, F. G., Ozbilge, E., and Cagin, T. “Fuzzy Theory in Fog Computing: Review, Taxonomy, and Open Issues.” IEEE Access, (2022).
  13. Ebneyousef, S., and Shirmarz, A. “A taxonomy of load balancing algorithms and approaches in fog computing: a survey.” Cluster Computing, (2023), 1–22.
  14. Ghanavati, S., Abawajy, J. H., and Izadi, D. “An energy aware task scheduling model using ant-mating optimization in fog computing environment.” IEEE Transactions on Services Computing, (2020).
  15. Alsamarai, N. A., Uçan, O. N., and Khalaf, O. F. “Bandwidth-Deadline IoT Task Scheduling in Fog–Cloud Computing Environment Based on the Task Bandwidth.” Wireless Personal Communications, (2023). https://doi.org/10.1007/s11277-023-10567-1
  16. Ahmed, O. H., Lu, J., Xu, Q., Ahmed, A. M., Rahmani, A. M., and Hosseinzadeh, M. “Using differential evolution and Moth–Flame optimization for scientific workflow scheduling in fog computing.” Applied Soft Computing, Vol. 112, , (2021), 107744. https://doi.org/10.1016/j.asoc.2021.107744
  17. Ghafari, R., and Mansouri, N. “E-AVOA-TS: Enhanced African vultures optimization algorithm-based task scheduling strategy for fog–cloud computing.” Sustainable Computing: Informatics and Systems, Vol. 40, , (2023), 100918.
  18. Guevara, J. C., Torres, R. D. S., Bittencourt, L. F., and Da Fonseca, N. L. S. “QoS-aware Task Scheduling based on Reinforcement Learning for the Cloud-Fog Continuum.” 2022 IEEE Global Communications Conference, GLOBECOM 2022 - Proceedings, (2022), 2328–2333. https://doi.org/10.1109/GLOBECOM48099.2022.10001644
  19. Saif, F. A., Latip, R., Hanapi, Z. M., and Shafinah, K. “Multi-objective grey wolf optimizer algorithm for task scheduling in cloud-fog computing.” IEEE Access, Vol. 11, , (2023), 20635–20646.
  20. Yadav, A. M., Tripathi, K. N., and Sharma, S. C. “An opposition-based hybrid evolutionary approach for task scheduling in fog computing network.” Arabian Journal for Science and Engineering, Vol. 48, No. 2, (2023), 1547–1562.
  21. Mousavi, S., Mood, S. E., Souri, A., and Javidi, M. M. “Directed search: a new operator in NSGA-II for task scheduling in IoT based on cloud-fog computing.” IEEE Transactions on Cloud Computing, (2022).
  22. Mujtiba, H. S., and Rasool, B. G. “Hybrid heuristic algorithm for cost-efficient QoS aware task scheduling in fog-cloud environment [J].” Journal of Computational Science, Vol. 64, , (2022), 101828.
  23. Bhardwaj, T., and Sharma, S. C. “Fuzzy logic-based elasticity controller for autonomic resource provisioning in parallel scientific applications: a cloud computing perspective.” Computers & Electrical Engineering, Vol. 70, , (2018), 1049–1073.
  24. Mothku, S. K., and Rout, R. R. “Fuzzy logic based adaptive duty cycling for sustainability in energy harvesting sensor actor networks.” Journal of King Saud University-Computer and Information Sciences, Vol. 34, No. 1, (2022), 1489–1497.
  25. Raju, M. R., and Mothku, S. K. “Delay and energy aware task scheduling mechanism for fog-enabled IoT applications: A reinforcement learning approach.” Computer Networks, Vol. 224, , (2023), 109603.
  26. Hortelano, D., de Miguel, I., Barroso, R. J. D., Aguado, J. C., Merayo, N., Ruiz, L., Asensio, A., Masip-Bruin, X., Fernández, P., and Lorenzo, R. M. “A comprehensive survey on reinforcement-learning-based computation offloading techniques in Edge Computing Systems.” Journal of Network and Computer Applications, Vol. 216, , (2023), 103669.
  27. Talaat, F. M., Saraya, M. S., Saleh, A. I., Ali, H. A., and Ali, S. H. “A load balancing and optimization strategy (LBOS) using reinforcement learning in fog computing environment.” Journal of Ambient Intelligence and Humanized Computing, Vol. 11, No. 11, (2020), 4951–4966.
  28. Zhao, W., and Stankovic, J. A. “Performance analysis of FCFS and improved FCFS scheduling algorithms for dynamic real-time computer systems.” In 1989 Real-Time Systems Symposium (pp. 156–157). IEEE Computer Society.
  29. Aladwani, T. “Types of task scheduling algorithms in cloud computing environment.” Scheduling Problems-New Applications and Trends, (2020).
  30. Stankovic, J. A., Spuri, M., Ramamritham, K., and Buttazzo, G. Deadline scheduling for real-time systems: EDF and related algorithms (Vol. 460). Springer Science & Business Media.
  31. Zhou, Z., Li, F., Zhu, H., Xie, H., Abawajy, J. H., and Chowdhury, M. U. “An improved genetic algorithm using greedy strategy toward task scheduling optimization in cloud environments.” Neural Computing and Applications, Vol. 32, , (2020), 1531–1541.
  32. Ghafari, R., and Mansouri, N. “An efficient task scheduling in fog computing using improved artificial hummingbird algorithm.” Journal of Computational Science, Vol. 74, , (2023), 102152.
  33. Chen, X., Cheng, L., Liu, C., Liu, Q., Liu, J., Mao, Y., and Murphy, J. “A woa-based optimization approach for task scheduling in cloud computing systems.” IEEE Systems journal, Vol. 14, No. 3, (2020), 3117–3128.
  34. Shojafar, M., Javanmardi, S., Abolfazli, S., and Cordeschi, N. “FUGE: A joint meta-heuristic approach to cloud job scheduling algorithm using fuzzy theory and a genetic method.” Cluster Computing, Vol. 18, No. 2, (2015), 829–844. https://doi.org/10.1007/s10586-014-0420-x
آمار
تعداد مشاهده مقاله: 396
تعداد دریافت فایل اصل مقاله: 145


سامانه مدیریت نشریات علمی. طراحی و پیاده سازی از سیناوب