Аналіз технологій та алгоритмів управління обчислювальними ресурсами при обробці відеоданих

Заголовок (англійською): 
Analysis of technologies and algorithms for management of computing resources in video data processing
Автор(и): 
Денисенко А. В.
Козлов О. В.
Автор(и) (англ): 
Denysenko A.
Kozlov O.
Ключові слова (укр): 
обробка відеоданих; управління обчислювальними ресурсами; планування завдань; розподіл ресурсів; балансування навантаження; інтелектуальні алгоритми
Ключові слова (англ): 
video data processing; management of computing resources task scheduling; resource allocation; load balancing; intelligent algorithms
Анотація (укр): 
Управління обчислювальними ресурсами при обробці відеоданих передбачає планування, розподіл, моніторинг і контроль різних типів обчислювальних ресурсів, таких як обчислювальна потужність, мережеві ресурси, зберігання даних тощо, щоб забезпечити їх ефективне використання при обробці великих обсягів відеоданих. Цей процес включає комплекс різноманітних завдань з обробки, аналізу та модифікації даних, що вимагає значної обчислювальної потужності. Відеодані мають великий розмір і потребують багато місця для зберігання, що може призвести до таких проблем, як затримки передавання і втрата даних. Для обробки відеоданих зазвичай потрібні спеціальні апаратні та програмні засоби, які можуть бути доволі дорогими та складними для налаштування. У пропонованій статті проведено комплексний аналіз наявних методів і засобів вирішення завдань розподілу й управління обчислювальними ресурсами в сучасних системах обробки відеоданих. Детально розглянуто сучасні алгоритми і технології, в т. ч. на основі штучного інтелекту, з визначенням їхніх основних переваг і недоліків.
Анотація (англ): 
Managing computational resources in video data processing involves planning, distributing, monitoring, and controlling various types of computational resources such as computing power, network resources, data storage, etc., to ensure their effective use in processing large volumes of video data. This process includes a complex of various tasks for processing, analyzing, and modifying data, which requires significant computational power. Video data are large in size and require a lot of storage space, which can lead to problems such as data transmission delays and data loss. Special hardware and software tools, which can be quite expensive and complex to configure, are usually required for processing video data. This paper provides a comprehensive analysis of existing methods and means for solving the tasks of distributing and managing computational resources in the modern systems of video data processing. Existing modern algorithms and technologies, including those based on artificial intelligence, are examined in detail, identifying their main advantages and disadvantages.
Публікатор: 
Київський національний університет будівництва і архітектури
Назва журналу, номер, рік випуску (укр): 
Управління розвитком складних систем, номер 59, 2024
Назва журналу, номер, рік випуску (англ): 
Management of Development of Complex Systems, number 59, 2024
Мова статті: 
English
Формат документа: 
application/pdf
Документ: 
217-224.pdf
Дата публікації: 
20 Ноябрь 2024
Номер збірника: 
59
Розділ: 
ТЕХНОЛОГІЯ УПРАВЛІННЯ РОЗВИТКОМ
Університет автора: 
Чорноморський національний університет імені Петра Могили, Миколаїв
Литература: 

1.     Sciamanna, F., Zanella, M., Massari, G. and Fornaciari, W. (2021). Managing the Resource Continuum in a Real Video Surveillance Scenario. 2021 24th Euromicro Conference on Digital System Design (DSD), Palermo, Italy, 2021, pp. 58–61, doi: 10.1109/DSD53832.2021.00018.

2.     Zhang, S., Liang, Y., Ge, J., Xiao, M. and Wu, J. (2020). Provably Efficient Resource Allocation for Edge Service Entities Using Hermes. IEEE/ACM Transactions on Networking, 28, 4, 1684–1697. Aug. 2020, doi: 10.1109/TNET.2020.2989307.

3.     Zhang, Y., Barusso, F., Collins, D., Ruffini, M. & DaSilva, L. A. (2018). Dynamic Allocation of Processing Resources in Cloud-RAN for a Virtualised 5G Mobile Network. 26th European Signal Processing Conference (EUSIPCO), Rome, Italy, 2018, pp. 782–786, doi: 10.23919/EUSIPCO.2018.8552959.

4.     Hossain, M. S., Hassan, M. M., Qurishi, M. A. & Alghamdi, A. (2012). Resource Allocation for Service Composition in Cloud-based Video Surveillance Platform. 2012 IEEE International Conference on Multimedia and Expo Workshops, Melbourne, VIC, Australia, pp. 408–412, doi: 10.1109/ICMEW.2012.77.

5.     Xu, Z., Mei, L., Liu, Y. & Hu, C. (2013). Video Structural Description: A Semantic Based Model for Representing and Organizing Video Surveillance Big Data. 2013 IEEE 16th International Conference on Computational Science and Engineering, Sydney, NSW, Australia, 2013, pp. 802–809, doi: 10.1109/CSE.2013.122.

6.     Minghao, Xia; Haibin, Liu; Jian, Li; Mingfei, Li. (2021). Research on Task Scheduling Algorithm Based on Multi-Time Period Merging. 2021 4th World Conference on Mechanical Engineering and Intelligent Manufacturing (WCMEIM). doi: 10.1109/WCMEIM54377.2021.00085

7.     Praveen, Kumar, Aditya, Tharad, Ulugbek, Mukhammadjonov, Seema, Rawat. (2021). Analysis on Resource Allocation for parallel processing and Scheduling in Cloud Computing. 2021 5th International Conference on Information Systems and Computer Networks (ISCON). doi: 10.1109/ISCON52037.2021.9702325

8.     Shalu, Rani, Dharminder, Kumar, Sakshi, Dhingra. (2022). A review on dynamic load balancing algorithms. 2022 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS). doi: 10.1109/ICCCIS56430.2022.10037671

9.     Zhou, Lin, Li, Zhen, Chen, Yingmei, Tan, Yuqin. (2014). The Video Monitoring System Based on Big Data Processing. 2014 7th International Conference on Intelligent Computation Technology and Automation. doi: 10.1109/ICICTA.2014.207

10. Haiyang, Zou, Mingdong, Li, Zhenhua, Li, Jianqing, Gao. (2018). Design of multi-intelligent data migration strategy based on SDN secondary mode. 2018 International Conference on Artificial Intelligence and Big Data (ICAIBD). doi: 10.1109/ICAIBD.2018.8396170

11. Tao, Li, Jingyu, Wang; Wei, Li, Tong, Xu, Qi, Qi. (2016). Load Prediction-Based Automatic Scaling Cloud Computing. 2016 International Conference on Networking and Network Applications (NaNA). doi: 10.1109/NaNA.2016.49

12. Manri, Cheon, Jong-Seok, Lee. (2018). Subjective and Objective Quality Assessment of Compressed 4K UHD Videos for Immersive Experience. IEEE Transactions on Circuits and Systems for Video Technology, 28, 7. doi: 10.1109/TCSVT.2017.2683504

13. Baofeng, Hui, Yuanliang, Ma. (2022). Image Recognition Technology of Monitoring Intelligent Alarm System Based on Deep Learning. 2022 11th International Conference of Information and Communication Technology (ICTech)). doi: 10.1109/ICTech55460.2022.00043

14. Manpreet, Singh Sehgal, Nandini, Bansal, Saloni, Dhingra, Ashish, Bansal, Pransh, Rastogi, Twinkle, Sehgal. (2022). Optimizing Round Robin Algorithm in Operating System. 2022 International Conference on Smart and Sustainable Technologies in Energy and Power Sectors (SSTEPS). doi: 10.1109/SSTEPS57475.2022.00051.

15. Yang, Zhang, Xing, Yang, Yuan, Xu, Yanlin, He, Mingqing, Zhang, Qunxiong, Zhu. (2024) Streaming Media Load Balancing with Improved Genetic Algorithm. 2024 36th Chinese Control and Decision Conference (CCDC). doi: 10.1109/CCDC62350.2024.10588365

16. Shoja, H., Nahid, H. & Azizi, R. (2014). A comparative survey on load balancing algorithms in cloud computing. Fifth International Conference on Computing, Communications and Networking Technologies (ICCCNT). doi:10.1109/icccnt.2014.6963138

17. Yang, Zhang, Xing, Yang, Yuan, Xu, Yanlin, He, Mingqing, Zhang, Qunxiong, Zhu. (2024). Streaming Media Load Balancing with Improved Genetic Algorithm. 2024 36th Chinese Control and Decision Conference (CCDC). doi: 10.1109/CCDC62350.2024.10588365

18. Bichitra, Mandal, Srinivas, Sethi, Ramesh, Kumar Sahoo. (2015). Architecture of efficient word processing using Hadoop MapReduce for big data applications. 2015 International Conference on Man and Machine Interfacing (MAMI). doi: 10.1109/MAMI.2015.7456612

19. Qiaojin, Guo, Jie Hu, Zhongyan, Liang. (2024). A Scalable Target Indexing and Retrieval System for Massive Video Data Processing based on Elasticsearch and Hadoop. 2024 IEEE 7th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC). doi: 10.1109/IAEAC59436.2024.10503896

20. Ivancsics, M., Brosch, N. & Gelautz, M. (2014). Efficient depth propagation in videos with GPU-acceleration. 2014 IEEE Visual Communications and Image Processing Conference. doi:10.1109/vcip.2014.7051557

21. Aggarwal, C. C. (2021). Artificial Intelligence. Springer International Publishing, 490. DOI 10.1007/978-3-030-72357-6.

22. Knight, K., Zhang, C., Holmes, G., Zhang, M.-L. (Eds.). Artificial Intelligence. Second CCF International Conference, ICAI 2019, Xuzhou, China, August 22-23, 2019, Proceedings, Springer Singapore, 2019, 298 p. DOI 10.1007/978-981-32-9298-7.

23. Zadeh, L. A., Abbasov, A. M., Yager, R. R., Shahbazova, S. N., Reformat, M. Z. (2014). Recent developments and new directions in soft computing, 466. DOI 10.1007/978-3-319-06323-2

24. Khochare, A., Sheshadri, K. R., Shriram, R. & Simmhan, Y. (2019). Dynamic Scaling of Video Analytics for Wide-Area Tracking in Urban Spaces. 2019 19th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID), Larnaca, Cyprus, 2019, pp. 76-81, doi: 10.1109/CCGRID.2019.00018.

25. Lee, H., Kim, Y. -S., Kim, M. & Lee, Y. (2021). Low-Cost Network Scheduling of 3D-CNN Processing for Embedded Action Recognition. IEEE Access, 9, 83901-83912. doi: 10.1109/ACCESS.2021.3087509.

26. Armin, Kappeler, Seunghwan, Yoo, Qiqin, Dai & Aggelos, K Katsaggelos. (2016). Video super-resolution with convolutional neural networks. IEEE Transactions on Computational Imaging, 2016.

27. Xu, Y., Zhang, H., Li, X., Yu, F. R., Leung, V. C. M. and Ji, H. (2023). Trusted Collaboration for MEC-Enabled VR Video Streaming: A Multi-Agent Reinforcement Learning Approach. IEEE Transactions on Vehicular Technology, 72, 9, 12167–12180. doi: 10.1109/TVT.2023.3267181.

28. Li, T., Wang, J., Li, W., Xu, T. & Qi, Q. (2016). Load Prediction-Based Automatic Scaling Cloud Computing. 2016 International Conference on Networking and Network Applications (NaNA). doi:10.1109/nana.2016.49

29. Mosayebi, A. & Pozveh, A. J. (2020). Heuristic Based Algorithm for SFC Allocation in 5G Experience Applications. 2020 6th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS), Mashhad, Iran, 2020, pp. 1–6, doi: 10.1109/ICSPIS51611.2020.9349535.

30. Luong, Thi Hong Lan, Tran, Manh Tuan, Tran, Thi Ngan, Le, Hoang Son, Nguyen, Long Giang, Vo, Truong Nhu Ngoc, Pham, Van Hai. (2020). A New Complex Fuzzy Inference System With Fuzzy Knowledge Graph and Extensions in Decision Making. IEEE Access, 8. doi: 10.1109/ACCESS.2020.3021097

31. Seungkyun, Lee, SuKyoung, Lee, Seung-Seob, Lee. (2021). Deadline-Aware Task Scheduling for IoT Applications in Collaborative Edge Computing. IEEE Wireless Communications Letters, 10, 10. doi: 10.1109/LWC.2021.3095496

32. Lin, C.-C., Wu, J.-J., Lin, J.-A., Song, L.-C. & Liu, P. (2012). Automatic Resource Scaling Based on Application Service Requirements. 2012 IEEE Fifth International Conference on Cloud Computing, doi: 10.1109/cloud.2012.32.

33. Farshchi, M., Schneider, J.-G., Weber, I. & Grundy, J. (2015). Experience report: Anomaly detection of cloud application operations using log and cloud metric correlation analysis. 2015 IEEE 26th International Symposium on Software Reliability Engineering (ISSRE).

References: 
  1. Sciamanna, F., Zanella, M., Massari, G. and Fornaciari, W. (2021). Managing the Resource Continuum in a Real Video Surveillance Scenario. 2021 24th Euromicro Conference on Digital System Design (DSD), Palermo, Italy, 2021, pp. 58–61, doi: 10.1109/DSD53832.2021.00018.
  2. Zhang, S., Liang, Y., Ge, J., Xiao, M. and Wu, J. (2020). Provably Efficient Resource Allocation for Edge Service Entities Using Hermes. IEEE/ACM Transactions on Networking, 28, 4, 1684–1697. Aug. 2020, doi: 10.1109/TNET.2020.2989307.
  3. Zhang, Y., Barusso, F., Collins, D., Ruffini, M. & DaSilva, L. A. (2018). Dynamic Allocation of Processing Resources in Cloud-RAN for a Virtualised 5G Mobile Network. 26th European Signal Processing Conference (EUSIPCO), Rome, Italy, 2018, pp. 782–786, doi: 10.23919/EUSIPCO.2018.8552959.
  4. Hossain, M. S., Hassan, M. M., Qurishi, M. A. & Alghamdi, A. (2012). Resource Allocation for Service Composition in Cloud-based Video Surveillance Platform. 2012 IEEE International Conference on Multimedia and Expo Workshops, Melbourne, VIC, Australia, pp. 408–412, doi: 10.1109/ICMEW.2012.77.
  5. Xu, Z., Mei, L., Liu, Y. & Hu, C. (2013). Video Structural Description: A Semantic Based Model for Representing and Organizing Video Surveillance Big Data. 2013 IEEE 16th International Conference on Computational Science and Engineering, Sydney, NSW, Australia, 2013, pp. 802–809, doi: 10.1109/CSE.2013.122.
  6. Minghao, Xia; Haibin, Liu; Jian, Li; Mingfei, Li. (2021). Research on Task Scheduling Algorithm Based on Multi-Time Period Merging. 2021 4th World Conference on Mechanical Engineering and Intelligent Manufacturing (WCMEIM). doi: 10.1109/WCMEIM54377.2021.00085
  7. Praveen, Kumar, Aditya, Tharad, Ulugbek, Mukhammadjonov, Seema, Rawat. (2021). Analysis on Resource Allocation for parallel processing and Scheduling in Cloud Computing. 2021 5th International Conference on Information Systems and Computer Networks (ISCON). doi: 10.1109/ISCON52037.2021.9702325
  8. Shalu, Rani, Dharminder, Kumar, Sakshi, Dhingra. (2022). A review on dynamic load balancing algorithms. 2022 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS). doi: 10.1109/ICCCIS56430.2022.10037671
  9. Zhou, Lin, Li, Zhen, Chen, Yingmei, Tan, Yuqin. (2014). The Video Monitoring System Based on Big Data Processing. 2014 7th International Conference on Intelligent Computation Technology and Automation. doi: 10.1109/ICICTA.2014.207
  10. Haiyang, Zou, Mingdong, Li, Zhenhua, Li, Jianqing, Gao. (2018). Design of multi-intelligent data migration strategy based on SDN secondary mode. 2018 International Conference on Artificial Intelligence and Big Data (ICAIBD). doi: 10.1109/ICAIBD.2018.8396170
  11. Tao, Li, Jingyu, Wang; Wei, Li, Tong, Xu, Qi, Qi. (2016). Load Prediction-Based Automatic Scaling Cloud Computing. 2016 International Conference on Networking and Network Applications (NaNA). doi: 10.1109/NaNA.2016.49
  12. Manri, Cheon, Jong-Seok, Lee. (2018). Subjective and Objective Quality Assessment of Compressed 4K UHD Videos for Immersive Experience. IEEE Transactions on Circuits and Systems for Video Technology, 28, 7. doi: 10.1109/TCSVT.2017.2683504
  13. Baofeng, Hui, Yuanliang, Ma. (2022). Image Recognition Technology of Monitoring Intelligent Alarm System Based on Deep Learning. 2022 11th International Conference of Information and Communication Technology (ICTech)). doi: 10.1109/ICTech55460.2022.00043
  14. Manpreet, Singh Sehgal, Nandini, Bansal, Saloni, Dhingra, Ashish, Bansal, Pransh, Rastogi, Twinkle, Sehgal. (2022). Optimizing Round Robin Algorithm in Operating System. 2022 International Conference on Smart and Sustainable Technologies in Energy and Power Sectors (SSTEPS). doi: 10.1109/SSTEPS57475.2022.00051.
  15. Yang, Zhang, Xing, Yang, Yuan, Xu, Yanlin, He, Mingqing, Zhang, Qunxiong, Zhu. (2024) Streaming Media Load Balancing with Improved Genetic Algorithm. 2024 36th Chinese Control and Decision Conference (CCDC). doi: 10.1109/CCDC62350.2024.10588365
  16. Shoja, H., Nahid, H. & Azizi, R. (2014). A comparative survey on load balancing algorithms in cloud computing. Fifth International Conference on Computing, Communications and Networking Technologies (ICCCNT). doi:10.1109/icccnt.2014.6963138
  17. Yang, Zhang, Xing, Yang, Yuan, Xu, Yanlin, He, Mingqing, Zhang, Qunxiong, Zhu. (2024). Streaming Media Load Balancing with Improved Genetic Algorithm. 2024 36th Chinese Control and Decision Conference (CCDC). doi: 10.1109/CCDC62350.2024.10588365
  18. Bichitra, Mandal, Srinivas, Sethi, Ramesh, Kumar Sahoo. (2015). Architecture of efficient word processing using Hadoop MapReduce for big data applications. 2015 International Conference on Man and Machine Interfacing (MAMI). doi: 10.1109/MAMI.2015.7456612
  19. Qiaojin, Guo, Jie Hu, Zhongyan, Liang. (2024). A Scalable Target Indexing and Retrieval System for Massive Video Data Processing based on Elasticsearch and Hadoop. 2024 IEEE 7th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC). doi: 10.1109/IAEAC59436.2024.10503896
  20. Ivancsics, M., Brosch, N. & Gelautz, M. (2014). Efficient depth propagation in videos with GPU-acceleration. 2014 IEEE Visual Communications and Image Processing Conference. doi:10.1109/vcip.2014.7051557
  21. Aggarwal, C. C. (2021). Artificial Intelligence. Springer International Publishing, 490. DOI 10.1007/978-3-030-72357-6.
  22. Knight, K., Zhang, C., Holmes, G., Zhang, M.-L. (Eds.). Artificial Intelligence. Second CCF International Conference, ICAI 2019, Xuzhou, China, August 22-23, 2019, Proceedings, Springer Singapore, 2019, 298 p. DOI 10.1007/978-981-32-9298-7.
  23. Zadeh, L. A., Abbasov, A. M., Yager, R. R., Shahbazova, S. N., Reformat, M. Z. (2014). Recent developments and new directions in soft computing, 466. DOI 10.1007/978-3-319-06323-2
  24. Khochare, A., Sheshadri, K. R., Shriram, R. & Simmhan, Y. (2019). Dynamic Scaling of Video Analytics for Wide-Area Tracking in Urban Spaces. 2019 19th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID), Larnaca, Cyprus, 2019, pp. 76-81, doi: 10.1109/CCGRID.2019.00018.
  25. Lee, H., Kim, Y. -S., Kim, M. & Lee, Y. (2021). Low-Cost Network Scheduling of 3D-CNN Processing for Embedded Action Recognition. IEEE Access, 9, 83901-83912. doi: 10.1109/ACCESS.2021.3087509.
  26. Armin, Kappeler, Seunghwan, Yoo, Qiqin, Dai & Aggelos, K Katsaggelos. (2016). Video super-resolution with convolutional neural networks. IEEE Transactions on Computational Imaging, 2016.
  27. Xu, Y., Zhang, H., Li, X., Yu, F. R., Leung, V. C. M. and Ji, H. (2023). Trusted Collaboration for MEC-Enabled VR Video Streaming: A Multi-Agent Reinforcement Learning Approach. IEEE Transactions on Vehicular Technology, 72, 9, 12167–12180. doi: 10.1109/TVT.2023.3267181.
  28. Li, T., Wang, J., Li, W., Xu, T. & Qi, Q. (2016). Load Prediction-Based Automatic Scaling Cloud Computing. 2016 International Conference on Networking and Network Applications (NaNA). doi:10.1109/nana.2016.49
  29. Mosayebi, A. & Pozveh, A. J. (2020). Heuristic Based Algorithm for SFC Allocation in 5G Experience Applications. 2020 6th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS), Mashhad, Iran, 2020, pp. 1–6, doi: 10.1109/ICSPIS51611.2020.9349535.
  30. Luong, Thi Hong Lan, Tran, Manh Tuan, Tran, Thi Ngan, Le, Hoang Son, Nguyen, Long Giang, Vo, Truong Nhu Ngoc, Pham, Van Hai. (2020). A New Complex Fuzzy Inference System With Fuzzy Knowledge Graph and Extensions in Decision Making. IEEE Access, 8. doi: 10.1109/ACCESS.2020.3021097
  31. Seungkyun, Lee, SuKyoung, Lee, Seung-Seob, Lee. (2021). Deadline-Aware Task Scheduling for IoT Applications in Collaborative Edge Computing. IEEE Wireless Communications Letters, 10, 10. doi: 10.1109/LWC.2021.3095496
  32. Lin, C.-C., Wu, J.-J., Lin, J.-A., Song, L.-C. & Liu, P. (2012). Automatic Resource Scaling Based on Application Service Requirements. 2012 IEEE Fifth International Conference on Cloud Computing, doi: 10.1109/cloud.2012.32.
  33. Farshchi, M., Schneider, J.-G., Weber, I. & Grundy, J. (2015). Experience report: Anomaly detection of cloud application operations using log and cloud metric correlation analysis. 2015 IEEE 26th International Symposium on Software Reliability Engineering (ISSRE).