Devising a method for balancing the load on a territorially distributed foggy environment
Creators
- 1. National Technical Uniersity "Kharkiv Polytechnic Institute", Ukraine
- 2. Kharkiv National University of Internal Affairs, Ukraine
- 3. Simon Kuznets Kharkiv National University of Economics, Ukraine
- 4. Ministry of Justice of Ukraine, Ukraine
- 5. Scientific Research Centre for Forensic on Intellectual Property of the Ministry of Justice of Ukraine, Ukraine
Description
This study solves the task to redistribute the load on a geographically distributed foggy environment in order to achieve a load balance of virtual clusters. The necessity and possibility of developing a universal and at the same time scientifically based approach to load balancing has been determined. Object of study: the process of redistribution of load in a foggy environment between virtual, geographically distributed clusters. A load balancing method makes it possible to reduce delays and decrease the time for completing tasks on foggy nodes, which brings task processing closer to real time. To solve the task, a mathematical model of the functioning of a separate cluster in a foggy environment has been built. As a result of modeling, the problem of finding the optimal distribution of tasks across the nodes of the virtual cluster was obtained. The limitations of the problem take into account the characteristics of the physical nodes of support for the virtual cluster. The process of distributing the additional load was also simulated through the graph representation of tasks entering virtual clusters. The task to devise a method for load transfer between virtual clusters within a foggy environment is solved using the proposed iterative algorithm for finding a suitable cluster and placing the load. The simulation results showed that the balance of the foggy environment when using the proposed method increases significantly provided the network load is small. The scope of application of the results includes geographically distributed foggy systems, in particular the foggy layer of the industrial Internet of Things. A necessary practical condition for using the proposed results is the non-exceeding the specified limit of the total load on the foggy medium, usually 70 %
Files
Devising a method for balancing the load on a territorially distributed foggy environment.pdf
Files
(732.4 kB)
Name | Size | Download all |
---|---|---|
md5:b4692bdffab0f51571f70bd791dc77f1
|
732.4 kB | Preview Download |
Additional details
References
- Kumar, N., Sharma, B., Narang, S. (2022). Emerging Communication Technologies for Industrial Internet of Things: Industry 5.0 Perspective. Lecture Notes in Networks and Systems, 107–122. doi: https://doi.org/10.1007/978-981-19-1142-2_9
- Qayyum, T., Trabelsi, Z., Waqar Malik, A., Hayawi, K. (2022). Mobility-aware hierarchical fog computing framework for Industrial Internet of Things (IIoT). Journal of Cloud Computing, 11 (1). doi: https://doi.org/10.1186/s13677-022-00345-y
- Chalapathi, G. S. S., Chamola, V., Vaish, A., Buyya, R. (2021). Industrial Internet of Things (IIoT) Applications of Edge and Fog Computing: A Review and Future Directions. Advances in Information Security, 293–325. doi: https://doi.org/10.1007/978-3-030-57328-7_12
- Lu, S., Wu, J., Wang, N., Duan, Y., Liu, H., Zhang, J., Fang, J. (2021). Resource provisioning in collaborative fog computing for multiple delay‐sensitive users. Software: Practice and Experience, 53(2), 243–262. doi: https://doi.org/10.1002/spe.3000
- Özdoğan, E. (2022). Cloud, Fog, and Edge Computing for IoT-Enabled Cognitive Buildings. IoT Edge Solutions for Cognitive Buildings, 23–52. doi: https://doi.org/10.1007/978-3-031-15160-6_2
- Kuchuk, G. A., Akimova, Yu. A., Klimenko, L. A. (2000). Method of optimal allocation of relational tables. Engineering Simulation, 17 (5), 681–689.
- Attar, H., Khosravi, M. R., Igorovich, S. S., Georgievan, K. N., Alhihi, M. (2021). E-Health Communication System with Multiservice Data Traffic Evaluation Based on a G/G/1 Analysis Method. Current Signal Transduction Therapy, 16 (2), 115–121. doi: https://doi.org/10.2174/1574362415666200224094706
- Kovalenko, A., Kuchuk, H., Kuchuk, N., Kostolny, J. (2021). Horizontal scaling method for a hyperconverged network. 2021 International Conference on Information and Digital Technologies (IDT). doi: https://doi.org/10.1109/idt52577.2021.9497534
- Raskin, L., Sukhomlyn, L., Sagaidachny, D., Korsun, R. (2021). Analysis of multi-threaded markov systems. Advanced Information Systems, 5 (4), 70–78. doi: https://doi.org/10.20998/2522-9052.2021.4.11
- Svyrydov, A., Kuchuk, H., Tsiapa, O. (2018). Improving efficienty of image recognition process: Approach and case study. 2018 IEEE 9th International Conference on Dependable Systems, Services and Technologies (DESSERT). doi: https://doi.org/10.1109/dessert.2018.8409201
- Li, G., Liu, Y., Wu, J., Lin, D., Zhao, S. (2019). Methods of Resource Scheduling Based on Optimized Fuzzy Clustering in Fog Computing. Sensors, 19 (9), 2122. doi: https://doi.org/10.3390/s19092122
- Proietti Mattia, G., Beraldi, R. (2023). P2PFaaS: A framework for FaaS peer-to-peer scheduling and load balancing in Fog and Edge computing. SoftwareX, 21, 101290. doi: https://doi.org/10.1016/j.softx.2022.101290
- Hoang, D., Dang, T. D. (2017). FBRC: Optimization of task Scheduling in Fog-Based Region and Cloud. 2017 IEEE Trustcom/BigDataSE/ICESS. doi: https://doi.org/10.1109/trustcom/bigdatase/icess.2017.360
- Sharma, S., Saini, H. (2019). A novel four-tier architecture for delay aware scheduling and load balancing in fog environment. Sustainable Computing: Informatics and Systems, 24, 100355. doi: https://doi.org/10.1016/j.suscom.2019.100355
- Khudov, H., Diakonov, O., Kuchuk, N., Maliuha, V., Furmanov, K., Mylashenko, I. et al. (2021). Method for determining coordinates of airborne objects by radars with additional use of ADS-B receivers. Eastern-European Journal of Enterprise Technologies, 4 (9 (112)), 54–64. doi: https://doi.org/10.15587/1729-4061.2021.238407
- Malik, U. M., Javed, M. A., Frnda, J., Rozhon, J., Khan, W. U. (2022). Efficient Matching-Based Parallel Task Offloading in IoT Networks. Sensors, 22 (18), 6906. doi: https://doi.org/10.3390/s22186906
- Liu, L., Chen, H., Xu, Z. (2022). SPMOO: A Multi-Objective Offloading Algorithm for Dependent Tasks in IoT Cloud-Edge-End Collaboration. Information, 13 (2), 75. doi: https://doi.org/10.3390/info13020075
- Ghenai, A., Kabouche, Y., Dahmani, W. (2018). Multi-user dynamic scheduling-based resource management for Internet of Things applications. 2018 International Conference on Internet of Things, Embedded Systems and Communications (IINTEC). doi: https://doi.org/10.1109/iintec.2018.8695308
- Yaloveha, V., Hlavcheva, D., Podorozhniak, A., Kuchuk, H. (2019). Fire Hazard Research of Forest Areas based on the use of Convolutional and Capsule Neural Networks. 2019 IEEE 2nd Ukraine Conference on Electrical and Computer Engineering (UKRCON). doi: https://doi.org/10.1109/ukrcon.2019.8879867
- Podorozhniak, A., Liubchenko, N., Kvochka, M., Suarez, I. (2021). Usage of intelligent methods for multispectral data processing in the field of environmental monitoring. Advanced Information Systems, 5 (3), 97–102. doi: https://doi.org/10.20998/2522-9052.2021.3.13
- Aburukba, R. O., AliKarrar, M., Landolsi, T., El-Fakih, K. (2020). Scheduling Internet of Things requests to minimize latency in hybrid Fog–Cloud computing. Future Generation Computer Systems, 111, 539–551. doi: https://doi.org/10.1016/j.future.2019.09.039
- Jamil, B., Shojafar, M., Ahmed, I., Ullah, A., Munir, K., Ijaz, H. (2019). A job scheduling algorithm for delay and performance optimization in fog computing. Concurrency and Computation: Practice and Experience, 32 (7). doi: https://doi.org/10.1002/cpe.5581