توازن بار در گره‌های مه با استفاده از الگوریتم یادگیری تقویتی
الموضوعات :نیلوفر طهماسبی پویا 1 , مهدی آقا صرام 2
1 - دانشگاه یزد
2 - دانشگاه یزد
الکلمات المفتاحية: تأخیر, توازن بار, گره مه, یادگیری تقویتی, Q-Learning,
ملخص المقالة :
محاسبات مه، حوزه تحقیقاتی نوظهوری برای ارائه خدمات محاسبات ابری به لبه‌های شبکه است. گره‌های مه جریان داده و درخواست‌های کاربر را در زمان واقعی پردازش می‌کنند. به منظور بهینه‌سازی بهره‌وری منابع و زمان پاسخ و افزایش سرعت و کارایی، وظایف باید به صورت متوازن بین گره‌های مه توزیع شوند، لذا در این مقاله، روشی جدید جهت بهبود توازن بار در محیط محاسبات مه پیشنهاد شده است. در الگوریتم پیشنهادی، هنگامی که وظیفه‌ای از طریق دستگاه‌های موبایل برای گره مه ارسال می‌شود، گره مه با استفاده از یادگیری تقویتی تصمیم می‌گیرد که آن وظیفه را خودش پردازش کند، یا این که پردازش آن را به یکی از گره‌های مه همسایه یا به ابر واگذار نماید. در بخش ارزیابی نشان داده شده که الگوریتم پیشنهادی با توزیع مناسب وظایف بین گره‌ها، تأخیر کمتری را برای اجرای وظایف نسبت به سایر روش‌های مقایسه‌شده به دست آورده است.
[1] I. Martinez, A. S. Hafid, and A. Jarray, "Design, resource management, and evaluation of fog computing systems: a survey," IEEE Internet of Things J., vol. 8, no. 4, pp. 2494-2516, Feb. 2020.
[2] A. Yousefpour, et al., "All one needs to know about fog computing and related edge computing paradigms: a complete survey," J. of Systems Architecture, vol. 98, pp. 289-330, Sept. 2019.
[3] D. Puthal, et al., "Secure authentication and load balancing of distributed edge data centers," J. of Parallel and Distributed Computing, vol. 124, pp. 60-69, Feb. 2019.
[4] N. Khattar, J. Sidhu, and J. Singh, "Toward energy-efficient cloud computing: a survey of dynamic power management and heuristics-based optimization techniques," The J. of Supercomputing, vol. 75, no. 8, pp. 4750-4810, Aug. 2019.
[5] M. Adhikari and T. Amgoth, "Heuristic-based load-balancing algorithm for IaaS cloud," Future Generation Computer Systems, vol. 81, pp. 156-165, Apr. 2018.
[6] S. Rasheed, et al., " A cloud-fog based smart grid model using max-min scheduling algorithm for efficient resource allocation," in Proc. Int. Conf. on Network-Based Information Systems, vol. 22, pp. 273-285, Sept. 2018.
[7] L. Abualigah et al., "Advances in meta-heuristic optimization algorithms in big data text clustering," Electronics, vol. 10, no. 2, Article ID: 101, 2021.
[8] M. Adhikari, S. Nandy, and T. Amgoth, "Meta heuristic-based task deployment mechanism for load balancing in IaaS cloud," J. of Network and Computer Applications, vol. 128, pp. 64-77, Feb. 2019.
[9] L. Shen, J. Li, Y. Wu, Z. Tang, and Y. Wang, "Optimization of artificial bee colony algorithm based load balancing in smart grid cloud," in Proc. of the IEEE Inovative Smart Grid Technologies-Asia, ISGT Asia, pp. 1131-1134, Chengdu, China, 21-24 May 2019.
[10] B. Kruekaew and W. Kimpan, "Multi-objective task scheduling optimization for load balancing in cloud computing environment using hybrid artificial bee colony algorithm with reinforcement learning," IEEE Access, vol. 10, pp. 17803-17818, 2022.
[11] S. R. Deshmukh, S. K. Yadav, and D. N. Kyatanvar, "Load balancing in cloud environs: optimal task scheduling via hybrid algorithm," International J. of Modeling, Simulation, and Scientific Computing, vol. 12, no. 2, Article ID: 2150008, Apr. 2021.
[12] M. M. Golchi, S. Saraeian, and M. Heydari, "A hybrid of firefly and improved particle swarm optimization algorithms for load balancing in cloud environments: performance evaluation," Computer Networks, vol. 162, Article ID: 106860, Oct. 2019.
[13] J. Y. Baek, G. Kaddoum, S. Garg, K. Kaur, and V. Gravel, "Managing fog networks using reinforcement learning based load balancing algorithm," in Proc. of the IEEE Wireless Communications and Networking Conf., WCNC’19, 7 pp., Marrakesh, Morocco, 15-18 Apr. 2019.
[14] X. Xu, S. Fu, Q. Cai, W. Tian, W. Liu, W. Dou, X. Sun, and A. X. Liu, "Dynamic resource allocation for load balancing in fog environment," Wireless Communications and Mobile Computing, vol. 2018, Article ID: 6421607, Apr. 2018.
[15] A. B. Manju and S. Sumathy, "Efficient load balancing algorithm for task preprocessing in fog computing environment," In Smart Intelligent Computing and Applications, Springer, Singapore, vol. 105, pp. 291-298, 2019.
[16] S. Sharma and H. Saini, "A novel four-tier architecture for delay aware scheduling and load balancing in fog environment," Sustainable Computing: Informatics and Systems, vol. 24, Article ID: 100355, Dec. 2019.
[17] F. M. Talaat, M. S. Saraya, A. I. Saleh, H. A. Ali, and S. H. Ali, "A load balancing and optimization strategy (LBOS) using reinforcement learning in fog computing environment," J. of Ambient Intelligence and Humanized Computing, vol. 11, no. 11, pp. 4951-4966, Nov. 2020.
[18] V. Divya and R. L. Sri, "ReTra: reinforcement based traffic load balancer in fog based network," in Proc. of the 10th Int. Conf. on Computing, Communication and Networking Technologies, ICCCNT’19, 6 pp., Kanpur, India, 6-8 Jul. 2019.
[19] H. Lu, C. Gu, F. Luo, W. Ding, and X. Liu, "Optimization of lightweight task offloading strategy for mobile edge computing based on deep reinforcement learning," Future Generation Computer Systems, vol. 102, pp. 847-861, Jan. 2020.
[20] R. Beraldi, C. Canali, R. Lancellotti, and G. P. Mattia, "A random walk based load balancing algorithm for fog computing," in Proc. of the Fifth Int. Conf. on Fog and Mobile Edge Computing, FMEC’20, pp. 46-53, Paris, France 20-23 Apr. 2020.
[21] 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," J. of Network and Systems Management, vol. 27, no. 4, pp. 883-929, Oct. 2019.
[22] H. Gupta, A. V. Dastjerdi, S. K. Ghosh, and R. Buyya, "iFogSim: a toolkit for modeling and simulation of resource management techniques in the Internet of Things, edge and fog computing environments," Software-Practice and Experience, vol. 47, no. 9, pp. 1275-1296, Sep. 2017.
[23] J. Xie, F. R. Yu, T. Huang, R. Xie, J. Liu, C. Wang, and Y. Liu, "A survey of machine learning techniques applied to software defined networking (SDN): research issues and challenges," IEEE Communications Surveys and Tutorials, vol. 21, no. 1, pp. 393-430, Aug. 2019.
[24] H. Ye, L. Liang, G. Y. Li, J. Kim, L. Lu, and M. Wu, "Machine learning for vehicular networks: recent advances and application examples," IEEE Vehicular Technology Magazine, vol. 13, no. 2, pp. 94-101, Apr. 2018.
[25] R. Sheikhpour, M. A. Sarram, S. Gharaghani, and M. A. Z. Chahooki, "A survey on semi-supervised feature selection methods," Pattern Recognition, vol. 64, pp. 141-158, Apr. 2017.
[26] A. Mebrek, M. Esseghir, and L. Merghem-Boulahia, "Energy-efficient solution based on reinforcement learning approach in fog networks," Proc. of the Fifth 15th Int. Wireless Communications & Mobile Computing Conf., IWCMC’19, pp. 2019-2024, Tangier, Morocco, 24-28 Jun. 2019.
[27] Y. Xu, W. Xu, Z. Wang, J. Lin, and S. Cui, "Load balancing for ultradense networks: a deep reinforcement learning-based approach," IEEE Internet of Things J., vol. 6, no. 6, pp. 9399-9412, Aug. 2019.
[28] R. Mahmud and R. Buyya, "Modeling and Simulation of Fog and Edge Computing Environments Using iFogSim Toolkit," Fog and Edge Computing, pp. 433-465, Jan. 2019.
[29] I. Tellioglu and H. A. Mantar, "A proportional load balancing for wireless sensor networks," in Proc. of the Fifth 3rd Int. Conf. on Sensor Technologies and Applications, pp. 514-519, Athens, Greece, 18-23 Jun. 2009.
