یک مدل برنامه ریزی خودمختار برای استقرار خدمات اینترنت اشیا در محاسبات مه: یک رویکرد مبتنی بر فرا ابتکاری
محورهای موضوعی : مهندسی کامپیوتر
منصوره زارع
1
,
یاسر علمی سولا
2
,
حسام حسن پور
3
1 - گروه کامپیوتر و فناوری اطلاعات، واحد سبزوار، دانشگاه آزاد اسلامی، سبزوار، ایران
2 - گروه کامپیوتر و فناوری اطلاعات، واحد سبزوار، دانشگاه آزاد اسلامی، سبزوار، ایران
3 - گروه کامپیوتر و فناوری اطلاعات، واحد سبزوار، دانشگاه آزاد اسلامی، سبزوار، ایران
کلید واژه: مدل خودمختار, رویکرد فراابتکاری, محاسبات مه, مکانیابی خدمات اینترنت اشیا,
چکیده مقاله :
دستگاه‌های مبتنی بر اینترنت اشیاء دائما در حال ارسال داده به ابر هستند. با این حال، متمرکز بودن مراکز داده ابر و فاصله زیاد تا محل ایجاد داده ها باعث کاهش کارایی این پارادایم در کاربردهای زمان واقعی شده است. محاسبات مه میتواند بدون درگیر کردن ابر منابع مورد نیاز دستگاه‌های اینترنت اشیاء را به صورت توزیع شده در لبه شبکه فراهم کند. بنابراین، پردازش، تجزیه و تحلیل و ذخیره-سازی به محل ایجاد داده ها و کاربران نهایی نزدیک شده و تاخیر کاهش می‌یابد. هر برنامه اینترنت اشیاء شامل مجموعه از سرویس‌های اینترنت اشیاء با الزامات مختلف کیفیت سرویس است که منابع مورد نیاز آنها میتوانند با استقرار روی گره‌های مه فراهم شوند. این مطالعه به چالش مکانیابی سرویس‌های اینترنت اشیاء به عنوان یک مدل برنامه‌ریزی خودمختار در محاسبات مه می‌پردازد. ما الگوریتم رقابت استعماری را به عنوان یک رویکرد مبتنی بر فراابتکاری برای حل این مسئله توسعه می دهیم. از آنجا که گره های مه با داشتن منابع کافی میتوانند میزبان چندین سرویس اینترنت اشیاء باشند، ما توزیع منابع را در فرایند مکانیابی در نظر می‌گیریم. الگوریتم پیشنهادی سرویس‌های اینترنت اشیاء را برای کاهش تاخیر اولویت بندی کرده و مسئله مکانیابی را به صورت چند-هدفه حل می کند. نتایج آزمایش‌ها نشان می دهد که الگوریتم ما میتواند به طور موثر عملکرد سیستم را بهبود داده و از بهترین نتایج الگوریتم‌های پیشرفته ادبیات بین 15% تا 31% اثربخشی بهتری داشته باشد.
IoT-based devices are constantly sending data to the cloud. However, the centralization of cloud data centers and the long distance to the location of data sources has reduced the efficiency of this paradigm in real-time applications. Fog computing can provide the resources needed by Internet of Things devices in a distributed manner at the edge of the network without involving the cloud. Therefore, processing, analysis and storage are closer to the source of data and end users cause the delay is reduced. Every Internet of Things program includes a set of Internet of Things services with different quality of service requirements, whose required resources can be provided by deploying on cloud nodes. This study deals with the challenge of locating Internet of Things services as an autonomous planning model in fog computing. We develop the colonial competition algorithm as a meta-heuristic approach to solve this problem. Since fog nodes with enough resources can host several IoT services, we consider resource distribution in the localization process. The proposed algorithm prioritizes Internet of Things services to reduce delay and solves the multi-objective positioning problem. The results of the experiments show that our algorithm can effectively improve the performance of the system and have 15% to 31% better effectiveness than the best results of the advanced algorithms in the literature.
[1] R. Basir, S. Qaisar, M. Ali, M. Aldwairi, M. I. Ashraf, A. Mahmood, and M. Gidlund, “Fog computing enabling industrial internet of things: State-of-the-art and research challenges,” Sensors, vol. 19, no. 21, p. 4807, 2019, doi: 10.3390/s19214807.
[2] T. Joyce and J. M. Herrmann, “A review of no free lunch theorems, and their implications for metaheuristic optimization,” Nature-inspired algorithms and applied optimization, vol. 744, pp. 27-51, 2018, doi: 10.1007/978-3-319-67669-2_2.
[3] S. Marchal, M. Miettinen, T. D. Nguyen, A. R. Sadeghi and N. Asokan,“Audi: Toward autonomous IoT device-type identification using periodic communication,”. IEEE Journal on Selected Areas in Communications, vol. 37, no. 6, pp. 1402-1412, 2019, doi: 10.1109/JSAC.2019.2904364..
[4] M. Etemadi, M. Ghobaei-Arani and A. Shahidinejad, “Resource provisioning for IoT services in the fog computing environment: An autonomic approach,”. Computer Communications, vol. 161, pp. 109-131, 2020, doi: 10.1016/j.comcom.2020.07.028 .
[5] S. Dlamini, J. Mwangama, N. Ventura and T. Magedanz, "Design of an Autonomous Management and Orchestration for Fog Computing," International Conference on Intelligent and Innovative Computing Applications (ICONIC), Mon Tresor, Mauritius, 2018, pp. 1-6, doi: 10.1109/ICONIC.2018.8601272.
[6] M. Ghobaei-Arani, M. Shamsi and A. A. Rahmanian, “An efficient approach for improving virtual machine placement in cloud computing environment,”. Journal of Experimental & Theoretical Artificial Intelligence, vol. 29, no. 6, pp. 1149-1171, 2017, doi: 10.1080/0952813X.2017.1310308.
[7] M. Slabicki and K. Grochla, "Performance evaluation of CoAP, SNMP and NETCONF protocols in fog computing architecture," NOMS 2016 - 2016 IEEE/IFIP Network Operations and Management Symposium, Istanbul, Turkey, 2016, pp. 1315-1319, doi: 10.1109/NOMS.2016.7503010.
[8] Z. Rezazadeh, D. Rahbari and M. Nickray, "Optimized Module Placement in IoT Applications Based on Fog Computing," Electrical Engineering (ICEE), Iranian Conference on, Mashhad, Iran, 2018, pp. 1553-1558, doi: 10.1109/ICEE.2018.8472469.
[9] H. Maaranen, K. Miettinen and A. Penttinen, “On initial populations of a genetic algorithm for continuous optimization problems,” Journal of Global Optimization, vol. 37, no. 3, pp. 405-436, 2007, doi: 10.1007/s10898-006-9056-6.
[10] H. Sami and A. Mourad, "Dynamic On-Demand Fog Formation Offering On-the-Fly IoT Service Deployment," in IEEE Transactions on Network and Service Management, vol. 17, no. 2, pp. 1026-1039, June 2020, doi: 10.1109/TNSM.2019.2963643.
[11] T. Dokeroglu, E. Seinc, T. Kucukyilmaz and A. Cosar, “A survey on new generation metaheuristic algorithms,”. Computers & Industrial Engineering, vol. 137, p. 106040, 2019, doi: 10.1016/j.cie.2019.106040.
[12] B. V. Natesha and R. M. R. Guddeti, “Adopting elitism-based Genetic Algorithm for minimizing multi-objective problems of IoT service placement in fog computing environment,”. Journal of Network and Computer Applications, vol. 178, p. 102972, 2021, doi: 10.1016/j.jnca.2020.102972.
[13] F. A. Salaht, F. Desprez and A. Lebre, “An overview of service placement problem in fog and edge computing,”. ACM Computing Surveys (CSUR), vol. 53, no. 3, pp. 1-35, 2020, doi: 10.1145/3391196.
[14] B. Jia, H. Hu, Y. Zeng, T. Xu and Y. Yang, “Double-matching resource allocation strategy in fog computing networks based on cost efficiency,” Journal of Communications and Networks, vol. 20, no. 3, pp. 237-246, 2018, doi: 10.1109/JCN.2018.000036.
[15] A. Yousefpour et al., "FOGPLAN: A Lightweight QoS-Aware Dynamic Fog Service Provisioning Framework," in IEEE Internet of Things Journal, vol. 6, no. 3, pp. 5080-5096, June 2019, doi: 10.1109/JIOT.2019.2896311.
[16] Y. Chen, Z. Li, B. Yang, K. Nai and K. Li, “A Stackelberg game approach to multiple resources allocation and pricing in mobile edge computing,”. Future Generation Computer Systems, vol. 108, pp. 273-287, 2020, doi: 10.1016/j.future.2020.02.045.
[17] M. Salimian, M. Ghobaei‐Arani and A. Shahidinejad, “Toward an autonomic approach for Internet of Things service placement using gray wolf optimization in the fog computing environment,” Software: Practice and Experience, vol. 51, no. 8, pp. 1745-1772, 2021, doi: 10.1002/spe.2986.
[18] M. Salimian, M. Ghobaei-Arani and A. Shahidinejad, “An Evolutionary Multi-objective Optimization Technique to Deploy the IoT Services in Fog-enabled Networks: An Autonomous Approach,”. Applied Artificial Intelligence, vol. 36, no. 1, 2022, doi: 10.1080/08839514.2021.2008149
[19] C. Liu, J. Wang, L. Zhou and A. Rezaeipanah, “Solving the multi-objective problem of IoT service placement in fog computing using cuckoo search algorithm,”. Neural Processing Letters, vol. 54, no. 3, pp. 1823-1854, 2022, doi: 10.1007/s11063-021-10708-2.
[20] D. Zhao, Q. Zou and M. Boshkani Zadeh, “A QoS-Aware IoT Service Placement Mechanism in Fog Computing Based on Open-Source Development Model,”. Journal of Grid Computing, vol. 20, no. 2, pp. 1-29, 2022, doi: 10.1007/s10723-022-09604-3.
[21] M. Ghobaei-Arani and A. Shahidinejad, “A cost-efficient IoT service placement approach using whale optimization algorithm in fog computing environment,”. Expert Systems with Applications, vol. 200, p. 117012, 2022, doi: 10.1016/j.eswa.2022.117012.
[22] M. Azimzadeh, A. Rezaee, S. J. Jassbi and M. Esnaashari, “Placement of IoT services in fog environment based on complex network features: a genetic-based approach,”. Cluster Computing, vol. 25, pp. 3423-3445, 2022, doi: 10.1007/s10586-022-03571-w.
[23] R. Basir, S. Qaisar, M. Ali, M. Aldwairi, M. I. Ashraf, A. Mahmood and M. Gidlund, “Fog computing enabling industrial internet of things: State-of-the-art and research challenges,”. Sensors, vol. 19, no. 21, p. 4807, 2019, doi: 10.3390/s19214807.
[24] S. Marchal, M. Miettinen, T. D. Nguyen, A. -R. Sadeghi and N. Asokan, "AuDI: Toward Autonomous IoT Device-Type Identification Using Periodic Communication," in IEEE Journal on Selected Areas in Communications, vol. 37, no. 6, pp. 1402-1412, June 2019, doi: 10.1109/JSAC.2019.2904364.
[25] M. Etemadi, M. Ghobaei-Arani and A. Shahidinejad, “Resource provisioning for IoT services in the fog computing environment: An autonomic approach,”. Computer Communications, vol. 161, pp. 109-131, 2020, doi: 10.1016/j.comcom.2020.07.028.
[26] S. Dlamini, J. Mwangama, N. Ventura and T. Magedanz, "Design of an Autonomous Management and Orchestration for Fog Computing," International Conference on Intelligent and Innovative Computing Applications (ICONIC), Mon Tresor, Mauritius, 2018, pp. 1-6, doi: 10.1109/ICONIC.2018.8601272.
[27] M. Ghobaei-Arani, M. Shamsi and A. A. Rahmanian, “An efficient approach for improving virtual machine placement in cloud computing environment,”. Journal of Experimental & Theoretical Artificial Intelligence, vol. 29, no. 6, pp. 1149-1171, 2017, doi: 10.1080/0952813X.2017.1310308.
[28] A. Shahidinejad, M. Ghobaei-Arani and M. Masdari, “Resource provisioning using workload clustering in cloud computing environment: a hybrid approach,”. Cluster Computing, vol. 24, no. 1, pp. 319-342, 2021, doi: 10.1007/s10586-020-03107-0.
[29] M. S. Aslanpour, S. E. Dashti, M. Ghobaei-Arani and A. A. Rahmanian, “Resource provisioning for cloud applications: a 3-D, provident and flexible approach,”. The Journal of Supercomputing, vol. 74, no. 12, pp. 6470-6501, 2018, doi: 10.1007/s11227-017-2156-x.
_||_[1] R. Basir, S. Qaisar, M. Ali, M. Aldwairi, M. I. Ashraf, A. Mahmood, and M. Gidlund, “Fog computing enabling industrial internet of things: State-of-the-art and research challenges,” Sensors, vol. 19, no. 21, p. 4807, 2019, doi: 10.3390/s19214807.
[2] T. Joyce and J. M. Herrmann, “A review of no free lunch theorems, and their implications for metaheuristic optimization,” Nature-inspired algorithms and applied optimization, vol. 744, pp. 27-51, 2018, doi: 10.1007/978-3-319-67669-2_2.
[3] S. Marchal, M. Miettinen, T. D. Nguyen, A. R. Sadeghi and N. Asokan,“Audi: Toward autonomous IoT device-type identification using periodic communication,”. IEEE Journal on Selected Areas in Communications, vol. 37, no. 6, pp. 1402-1412, 2019, doi: 10.1109/JSAC.2019.2904364..
[4] M. Etemadi, M. Ghobaei-Arani and A. Shahidinejad, “Resource provisioning for IoT services in the fog computing environment: An autonomic approach,”. Computer Communications, vol. 161, pp. 109-131, 2020, doi: 10.1016/j.comcom.2020.07.028 .
[5] S. Dlamini, J. Mwangama, N. Ventura and T. Magedanz, "Design of an Autonomous Management and Orchestration for Fog Computing," International Conference on Intelligent and Innovative Computing Applications (ICONIC), Mon Tresor, Mauritius, 2018, pp. 1-6, doi: 10.1109/ICONIC.2018.8601272.
[6] M. Ghobaei-Arani, M. Shamsi and A. A. Rahmanian, “An efficient approach for improving virtual machine placement in cloud computing environment,”. Journal of Experimental & Theoretical Artificial Intelligence, vol. 29, no. 6, pp. 1149-1171, 2017, doi: 10.1080/0952813X.2017.1310308.
[7] M. Slabicki and K. Grochla, "Performance evaluation of CoAP, SNMP and NETCONF protocols in fog computing architecture," NOMS 2016 - 2016 IEEE/IFIP Network Operations and Management Symposium, Istanbul, Turkey, 2016, pp. 1315-1319, doi: 10.1109/NOMS.2016.7503010.
[8] Z. Rezazadeh, D. Rahbari and M. Nickray, "Optimized Module Placement in IoT Applications Based on Fog Computing," Electrical Engineering (ICEE), Iranian Conference on, Mashhad, Iran, 2018, pp. 1553-1558, doi: 10.1109/ICEE.2018.8472469.
[9] H. Maaranen, K. Miettinen and A. Penttinen, “On initial populations of a genetic algorithm for continuous optimization problems,” Journal of Global Optimization, vol. 37, no. 3, pp. 405-436, 2007, doi: 10.1007/s10898-006-9056-6.
[10] H. Sami and A. Mourad, "Dynamic On-Demand Fog Formation Offering On-the-Fly IoT Service Deployment," in IEEE Transactions on Network and Service Management, vol. 17, no. 2, pp. 1026-1039, June 2020, doi: 10.1109/TNSM.2019.2963643.
[11] T. Dokeroglu, E. Seinc, T. Kucukyilmaz and A. Cosar, “A survey on new generation metaheuristic algorithms,”. Computers & Industrial Engineering, vol. 137, p. 106040, 2019, doi: 10.1016/j.cie.2019.106040.
[12] B. V. Natesha and R. M. R. Guddeti, “Adopting elitism-based Genetic Algorithm for minimizing multi-objective problems of IoT service placement in fog computing environment,”. Journal of Network and Computer Applications, vol. 178, p. 102972, 2021, doi: 10.1016/j.jnca.2020.102972.
[13] F. A. Salaht, F. Desprez and A. Lebre, “An overview of service placement problem in fog and edge computing,”. ACM Computing Surveys (CSUR), vol. 53, no. 3, pp. 1-35, 2020, doi: 10.1145/3391196.
[14] B. Jia, H. Hu, Y. Zeng, T. Xu and Y. Yang, “Double-matching resource allocation strategy in fog computing networks based on cost efficiency,” Journal of Communications and Networks, vol. 20, no. 3, pp. 237-246, 2018, doi: 10.1109/JCN.2018.000036.
[15] A. Yousefpour et al., "FOGPLAN: A Lightweight QoS-Aware Dynamic Fog Service Provisioning Framework," in IEEE Internet of Things Journal, vol. 6, no. 3, pp. 5080-5096, June 2019, doi: 10.1109/JIOT.2019.2896311.
[16] Y. Chen, Z. Li, B. Yang, K. Nai and K. Li, “A Stackelberg game approach to multiple resources allocation and pricing in mobile edge computing,”. Future Generation Computer Systems, vol. 108, pp. 273-287, 2020, doi: 10.1016/j.future.2020.02.045.
[17] M. Salimian, M. Ghobaei‐Arani and A. Shahidinejad, “Toward an autonomic approach for Internet of Things service placement using gray wolf optimization in the fog computing environment,” Software: Practice and Experience, vol. 51, no. 8, pp. 1745-1772, 2021, doi: 10.1002/spe.2986.
[18] M. Salimian, M. Ghobaei-Arani and A. Shahidinejad, “An Evolutionary Multi-objective Optimization Technique to Deploy the IoT Services in Fog-enabled Networks: An Autonomous Approach,”. Applied Artificial Intelligence, vol. 36, no. 1, 2022, doi: 10.1080/08839514.2021.2008149
[19] C. Liu, J. Wang, L. Zhou and A. Rezaeipanah, “Solving the multi-objective problem of IoT service placement in fog computing using cuckoo search algorithm,”. Neural Processing Letters, vol. 54, no. 3, pp. 1823-1854, 2022, doi: 10.1007/s11063-021-10708-2.
[20] D. Zhao, Q. Zou and M. Boshkani Zadeh, “A QoS-Aware IoT Service Placement Mechanism in Fog Computing Based on Open-Source Development Model,”. Journal of Grid Computing, vol. 20, no. 2, pp. 1-29, 2022, doi: 10.1007/s10723-022-09604-3.
[21] M. Ghobaei-Arani and A. Shahidinejad, “A cost-efficient IoT service placement approach using whale optimization algorithm in fog computing environment,”. Expert Systems with Applications, vol. 200, p. 117012, 2022, doi: 10.1016/j.eswa.2022.117012.
[22] M. Azimzadeh, A. Rezaee, S. J. Jassbi and M. Esnaashari, “Placement of IoT services in fog environment based on complex network features: a genetic-based approach,”. Cluster Computing, vol. 25, pp. 3423-3445, 2022, doi: 10.1007/s10586-022-03571-w.
[23] R. Basir, S. Qaisar, M. Ali, M. Aldwairi, M. I. Ashraf, A. Mahmood and M. Gidlund, “Fog computing enabling industrial internet of things: State-of-the-art and research challenges,”. Sensors, vol. 19, no. 21, p. 4807, 2019, doi: 10.3390/s19214807.
[24] S. Marchal, M. Miettinen, T. D. Nguyen, A. -R. Sadeghi and N. Asokan, "AuDI: Toward Autonomous IoT Device-Type Identification Using Periodic Communication," in IEEE Journal on Selected Areas in Communications, vol. 37, no. 6, pp. 1402-1412, June 2019, doi: 10.1109/JSAC.2019.2904364.
[25] M. Etemadi, M. Ghobaei-Arani and A. Shahidinejad, “Resource provisioning for IoT services in the fog computing environment: An autonomic approach,”. Computer Communications, vol. 161, pp. 109-131, 2020, doi: 10.1016/j.comcom.2020.07.028.
[26] S. Dlamini, J. Mwangama, N. Ventura and T. Magedanz, "Design of an Autonomous Management and Orchestration for Fog Computing," International Conference on Intelligent and Innovative Computing Applications (ICONIC), Mon Tresor, Mauritius, 2018, pp. 1-6, doi: 10.1109/ICONIC.2018.8601272.
[27] M. Ghobaei-Arani, M. Shamsi and A. A. Rahmanian, “An efficient approach for improving virtual machine placement in cloud computing environment,”. Journal of Experimental & Theoretical Artificial Intelligence, vol. 29, no. 6, pp. 1149-1171, 2017, doi: 10.1080/0952813X.2017.1310308.
[28] A. Shahidinejad, M. Ghobaei-Arani and M. Masdari, “Resource provisioning using workload clustering in cloud computing environment: a hybrid approach,”. Cluster Computing, vol. 24, no. 1, pp. 319-342, 2021, doi: 10.1007/s10586-020-03107-0.
[29] M. S. Aslanpour, S. E. Dashti, M. Ghobaei-Arani and A. A. Rahmanian, “Resource provisioning for cloud applications: a 3-D, provident and flexible approach,”. The Journal of Supercomputing, vol. 74, no. 12, pp. 6470-6501, 2018, doi: 10.1007/s11227-017-2156-x.
