زمانبندی وظایف در سیستمهای توزیع شده لایه مه و ابر محاسباتی با استفاده از الگوریتم بهینه‌سازی سوسک سرگین
الموضوعات :
محسن اقبالی
1
(دانشجوی دکتری مهندسی کامپیوتر، گروه مهندسی کامپیوتر، واحد میبد، دانشگاه آزاد اسلامی، میبد، ایران)
رضا عزیزی
2
(استادیار گروه کامپیوتر، گروه مهندسی کامپیوتر، واحد میبد، دانشگاه آزاد اسلامی، میبد، ایران)
الکلمات المفتاحية: اینترنت اشیاء, الگوریتم بهینه‌سازی سوسک سرگین, زمانبندی وظایف, لایه ابر, لایه مه,
ملخص المقالة :
در چند سال گذشته اینترنت اشیاء رشد قابل توجه‌ای داشته است و تعداد زیادی شی هوشمند به آن متصل شده است. رایانش ابری به عنوان یک سیستم پردازش داده‌ها در اینترنت اشیاء است با این حال، سرورها در الگوی محاسبات ابری معمولاً در یک فاصله فیزیکی طولانی از دستگاه‌های اینترنت اشیاء قرار دارند و تأخیر زیاد ناشی از فواصل طولانی نمی‌تواند به طور مؤثر برنامه‌های اینترنت اشیاء بلادرنگ را برآورده کند. به دلیل این مسائل، محاسبات لبه و مه به عنوان فناوری محاسباتی محبوب در زمینه اینترنت اشیا ظاهر شده است. یکی از چالش‌های مهم اینترنت اشیاء، مسئله زمانبندی وظایف در لایه مه و ابر است. در روش پیشنهادی برای تخصیص منابع آزاد از شبکه عصبی LSTM استفاده می‌شود و برای زمانبندی بهینه وظایف در لایه ابر و مه از الگوریتم بهینه‌سازی سوسک سرگین استفاده می‌شود. آزمایشات نشان می‌دهد که در مجموعه داده HPC2N دقت، حساسیت و صحت روش پیشنهادی برای پیش بینی وضعیت منابع به ترتیب برابر 94/72 درصد، 93/21 درصد و 91/64 درصد است. در مجموعه داده NASA دقت، حساسیت و صحت پیش بینی روش پیشنهادی در تخصیص منابع به ترتیب برابر 95/68 درصد، 94/61 درصد و 92/37 درصد است. روش پیشنهادی نسبت به روشهای RNN، 1DCNN، MLP دقت بیشتری در تخصیص منابع برای زمانبندی دارد. شاخص Makespan روش پیشنهادی نسبت به روشهای AO_AVOA، AVOA، PSO، HHO و FA مقدار کمتری و بهتری را در زمانبندی وظایف نشان می‌دهد.
[1] A. S. Abohamama, A. El-Ghamry & E. Hamouda, “Real-time task scheduling algorithm for IoT-based applications in the cloud–fog environment,” Journal of Network and Systems Management, vol. 30, no.4,1-35,54, 27 May 2022, doi: 10.1007/s10922-022-09664-6.
[2] D. R. Prapti, A. R. Mohamed Shariff, H. Che Man, N. M. Ramli, T. Perumal, & M. Shariff, “Internet of Things (IoT)‐based aquaculture: An overview of IoT application on water quality monitoring,” Reviews in Aquaculture, vol. 14, no. 2, pp. 979-992, 19 November 2021, doi: 10.1111/raq.12637.
[3] I. Attiya, M. Abd Elaziz, L. Abualigah, T. N. Nguyen, & A. A. Abd El-Latif, “An improved hybrid swarm intelligence for scheduling iot application tasks in the cloud,” IEEE Transactions on Industrial Informatics, vol. 18, no. 9, pp. 6264-6272, 04 February 2022, doi: 10.1109/TII.2022.3148288.
[4] A. Rahimikhanghah, M. Tajkey, B. Rezazadeh, & A. M. Rahmani, “Resource scheduling methods in cloud and fog computing environments: a systematic literature review,” Cluster Computing, vol. 25, pp. 911-945, 1-35. April 2022, doi: 10.1007/s10586-021-03467-1.
[5] M. T. Zhou, T. F. Ren, Z. M. Dai, & X. Y. Feng, “Task scheduling and resource balancing of fog computing in smart factory,” Mobile Networks and Applications, vol. 28, no. 1, pp. 19-30. February 2023, doi: 10.1007/s11036-022-01992-w.
[6] S. Subbaraj, R. Thiyagarajan, & M. Rengaraj, “A smart fog computing based real-time secure resource allocation and scheduling strategy using multi-objective crow search algorithm,” Journal of Ambient Intelligence and Humanized Computing, vol. 14, no. 2, pp. 1003-1015. February 2023, doi: 10.1007/s12652-021-03354-y.
[7] M. R. Raju, & S. K. Mothku, “Delay and energy aware task scheduling mechanism for fog-enabled IoT applications: A reinforcement learning approach,” Computer Networks, vol. 224, 109603, 8 February 2023, doi: 10.1016/j.comnet.
[8] H. Wadhwa, & R. Aron, “Optimized task scheduling and preemption for distributed resource management in fog-assisted IoT environment,” The Journal of Supercomputing, vol. 79, no. 2, pp. 2212-2250, February 2023, doi : 10.1007/s11227-022-04747-2.
[9] T. K. Vashishth, , V. Sharma, K. K. Sharma, B. Kumar, S. Chaudhary, & R. Panwar, (2024). “Intelligent Resource Allocation and Optimization for Industrial Robotics Using AI and Blockchain,” In AI and Blockchain Applications in Industrial Robotics IGI Global, pp. 82-110, December 2023, doi : 10.4018/979-8-3693-0659-8.ch004.
[10] Saifeng, Z. (2024). “AQINM: an adaptive QoS management framework based on intelligent negotiation and monitoring in cloud,” International Journal of Information Technology and Management, vol. 23, no. 1, pp. 33-47, 22 January 2024, doi: 10.1504/IJITM.2024.136183.
[11] E. Khezri, R. O. Yahya, H. Hassanzadeh, M. Mohaidat, S. Ahmadi, & M. Trik, “DLJSF: Data-Locality Aware Job Scheduling IoT tasks in fog-cloud computing environments,” Results in Engineering, vol. 21, 101780. 24 January 2024, doi: 10.1016/j.rineng.2024.101780.
[12] Y. Lin, Y. Xu, J. Zhu, X. Wang, L. Wang, & G. Hu, “MLATSO: A method for task scheduling optimization in multi-load AGVs-based systems,” Robotics and Computer-Integrated Manufacturing, vol. 79, 102397, February 2023, doi: 10.1016/j.rcim.2022.102397.
[13] Y. Shen, & H. Li, “A multi-strategy genetic algorithm for solving multi-point dynamic aggregation problems with priority relationships of tasks,” Electronic Research Archive, vol.32, no. 1, pp. 445-472, 2024, doi: 10.3934/era.2024022.
[14] X. Fu, Y. Sun, H. Wang, & H. Li, “Task scheduling of cloud computing based on hybrid particle swarm algorithm and genetic algorithm,” Cluster Computing, vol. 26, no. 5, pp. 2479-2488, October 2023, doi: 10.1007/s10586-020-03221-z.
[15] S. Mangalampalli, S. K. Swain, G. R. Karri, & S. Mishra, “SLA Aware Task-Scheduling Algorithm in Cloud Computing Using Whale Optimization Algorithm,” Scientific Programming, vol. 2023, 20 Apr 2023, doi: 10.1155/2023/8830895.
[16] S. Mangalampalli, G. R. Karri, S. N. Mohanty, S. Ali, M. I. Khan, D. Abduvalieva, F. A. Awwad & E. A. Ismail, “Fault tolerant trust based task scheduler using Harris Hawks optimization and deep reinforcement learning in multi cloud environment,” Scientific Reports, vol. 13, no. 1, 19179. 06 November 2023, doi.org/10.1038/s41598-023-46284-9.
[17] J. Xue, & B. Shen, “Dung beetle optimizer: A new meta-heuristic algorithm for global optimization,” The Journal of Supercomputing, vol. 79, no. 7, pp. 7305-7336, May 2023, doi.org/10.1007/s11227-022-04959-6.
[18] Z. Yin, F. Xu, Y. Li, C. Fan, F. Zhang, G.Han, & Y. Bi, “A multi-objective task scheduling strategy for intelligent production line based on cloud-fog computing,” Sensors, vo. 22, no. 4, 1555, 15 February 2022, doi: 10.3390/s22041555.
[19] A. A. Mutlag, M. Khanapi Abd Ghani, M. A. Mohammed, M. S. Maashi, O. Mohd, S. A. Mostafa, , k. h. Abdulkareem, G. Marques, & I. de la Torre Díez, (2020). “MAFC: Multi-agent fog computing model for healthcare critical tasks management,” Sensors, vol. 20, no. 7, 1853, 25 March 2020, doi.org: 10.3390/s20071853.
[20] X. Ma, H. Gao, H. Xu, & M. Bian, “An IoT-based task scheduling optimization scheme considering the deadline and cost-aware scientific workflow for cloud computing,” EURASIP Journal on Wireless Communications and Networking, vol. 2019, no, 249, pp. 1-19, 08 November 2019, doi: 10.1186/s13638-019-1557-3.
[21] M. Hosseini Shirvani, “A survey study on task scheduling schemes for workflow executions in cloud computing environment: classification and challenges,” The Journal of Supercomputing, pp. 1-54, 07 December 2023 , doi: 10.1007/s11227-023-05806-y.
[22] M. Hosseinzadeh, E. Azhir, J. Lansky, S. Mildeova, O. H. Ahmed, M. H. Malik, & F. Khan, “Task Scheduling Mechanisms for Fog Computing: A Systematic Survey,” IEEE Access, vol. 11, pp. 50994–51017, 18 May 2023, doi: 10.1109/ACCESS.2023.3277826.
[23] Zhou, M. T., Ren, T. F., Dai, Z. M., & Feng, X. Y. (2023). “Task scheduling and resource balancing of fog computing in smart factory,” Mobile Networks and Applications, vol. 28, no. 1, pp. 19-30, February 2023, doi: 10.1007/s11036-022-01992-w.
[24] Z. A. Khan, I. A. Aziz, & N. A. B. Osman, “A Review on Task Scheduling Techniques in Cloud and Fog Computing: Taxonomy, Tools, Open Issues, Challenges, and Future Directions,” IEEE Access, vol. 11, pp. 143417 – 143445, 18 December 2023, doi: 10.1109/ACCESS.2023.3343877.
[25] T. Salehnia, A. Seyfollahi, , S. Raziani, A. Noori, A. Ghaffari, A. R. Alsoud, & L. Abualigah, “An optimal task scheduling method in IoT-Fog-Cloud network using multi-objective moth-flame algorithm,” Multimedia Tools and Applications, pp. 1-22, 11 September 2023, doi: 10.1007/s11042-023-16971-w.
[26] F. Ramezani Shahidani, A. Ghasemi, A. Toroghi Haghighat, & A. Keshavarzi, “Task scheduling in edge-fog-cloud architecture: a multi-objective load balancing approach using reinforcement learning algorithm,” Computing, vol. 105, no. 6, pp. 1337-1359, June 2023, doi: 10.1007/s00607-022-01147-5.
[27] B. M. Nguyen, T. Nguyen, Q. H. Vu, H. H. Tran, H. Vo, H. T. T. Binh, S. Yu , Z. Wu, “A novel nature-inspired algorithm for optimal task scheduling in fog-cloud blockchain system,” IEEE Internet of Things Journal, vol. 11, 06 July 2023, doi: 10.1109/JIOT.2023.3292872.
[28] J. Z. Ahmadabadi, S. E. Mood, & A. Souri, “Star-quake: A new operator in multi-objective gravitational search algorithm for task scheduling in IoT based cloud-fog computing system,” IEEE Transactions on Consumer Electronics, January 2023, doi: 10.1109/TCE.2023.3321708.
[29] R. Ghafari, & N. Mansouri, “E-AVOA-TS: Enhanced African vultures optimization algorithm-based task scheduling strategy for fog–cloud computing,” Sustainable Computing: Informatics and Systems, vol. 40, 100918, December 2023, doi: 10.1016/j.suscom.2023.100918.
[30] Q. Liu, H. Kosarirad, S. Meisami, K. A. Alnowibet, & A. N. Hoshyar, “An Optimal Scheduling Method in IoT-Fog-Cloud Network Using Combination of Aquila Optimizer and African Vultures Optimization,” Processes, vol. 11, no. 4,1162, 10 April 2023, doi: 10.3390/pr11041162.
[31] Z. Wang, M. Goudarzi, M. Gong, & R. Buyya, “Deep Reinforcement Learning-based scheduling for optimizing system load and response time in edge and fog computing environments,” Future Generation Computer Systems, vol. 152, pp. 55-69, Mtch 2024, doi: /10.1016/j.future.2023.10.012.
[32] M. Osmanpoor, A. Shameli-Sendi, & F. Faraji Daneshgar, “Convergence of the Harris hawks optimization algorithm and fuzzy system for cloud-based task scheduling enhancement,” Cluster Computing, pp. 1-15, 09 January 2024, doi.org/10.1007/s10586-023-04225-1.
[33] D. Sanchez Narvaez, C. Villaseñor, C. Lopez-Franco, & N. Arana-Daniel, “Order-Based Schedule of Dynamic Topology for Recurrent Neural Network,” Algorithms, vol. 16, no. 5, 231. 28 April 2023, doi: 10.3390/a16050231.
[34] B. M. Nguyen, , H. Thi Thanh Binh, T. The Anh, & D. Bao Son, “Evolutionary algorithms to optimize task scheduling problem for the IoT based bag-of-tasks application in cloud–fog computing environment,” Applied Sciences, vol. 9, no. 9, 1730, 26 April 2019, doi: 10.3390/app9091730.
[35] L. Li, L. Liu, Y. Shao, X. Zhang, Y. Chen, C. Guo, & H. Nian, “Enhancing Swarm Intelligence for Obstacle Avoidance with Multi-Strategy and Improved Dung Beetle Optimization Algorithm in Mobile Robot Navigation,” Electronics, vol. 12, no. 21, 4462, 30 October 2023 , doi: 10.3390/electronics12214462.
[36] M. S. Kumar, & G. R. Karri, “Eeoa: cost and energy efficient task scheduling in a cloud-fog framework,” Sensors, vol. 23, no. 5, 2445, 22 February 2023, doi: 10.3390/s23052445.
