№1, 2026
MULTI-OBJECTIVE RESOURCE ALLOCATION IN EDGE COMPUTING USING TF-IDF SCHEME
Recently, the rapid increase in the number of mobile phones and IoT devices connected to networks has led to a decrease in the throughput capacity of Internet network channels and delays in delivering processed data to users. To eliminate these delays in network channels, edge computing systems are widely used. Edge computing systems enable data to be processed on computing devices close to the users, without being sent to remote cloud servers. These systems reduce network latency, allow fast real-time data processing, and enable a reduction in the costs of services. This paper proposes a model of an edge computing system based on data processing centers. Two criteria, resource usage frequency and number of users, are taken as the basis for the allocation of cloud resources in edge computing networks. For balancing these criteria, the TF-IDF scheme is used. The proposed approach is formalized as a two-objective optimization model. An algorithm for selecting the optimal solution from the Pareto-optimal front is employed (pp.3-13).
Keywords: Cloud computing, Edge computing network, Resource allocation, Multi-objective optimization, Pareto front, Usage frequency, Number of users, TF-IDF scheme
References
- Abbas, N., Zhang, Y., Taherkordi, A., & Skeie, T. (2018). Mobile edge computing: A survey. IEEE Internet of Things Journal, 5(1), 450-465. https://doi.org/ 10.1109/JIOT.2017.2750180
- Abusaimeh, H. (2022). Computation Offloading for Mobile Cloud Computing Frameworks and Techniques. TEM Journal, 11(3), 1042-1046. https://doi.org/10.18421/TEM113-08
- Akomolafe, O. P., Abodunrin, M. O. (2017). A Hybrid Cryptographic Model for Data Storage in Mobile Cloud Computing, International Journal of Computer Network and Information Security, 9(6), 53-60. https://doi.org/10.5815/ijcnis.2017.06.06
- Alakbarov, R. G. (2021). Challenges of mobile devices' resources and in communication channels and their solutions. International Journal of Computer Network and Information Security, 13(1), 39-46. https://doi.org/10.5815/ijcnis.2021.01.04
- Alguliyev, R. M., & Alakbarov, R. G. (2023). Integer programming models for task scheduling and resource allocation in mobile cloud computing. International Journal of Computer Network and Information Security, 15(5), 13-26. https://doi.org/10.5815/ijcnis.2023.05.02
- Alguliyev, R. M., Aliguliyev, R. M., & Alakbarov, R. G. (2023). Constrained k-means algorithm for resource allocation in mobile cloudlets. Kybernetika, 59(1), 88-109. https://doi.org/10.14736/kyb-2023-1-0088
- Andriulo, F. C., Fiore, M., Mongiello, M., Traversa, E., & Zizzo, V. (2024). Edge computing and cloud computing for ınternet of things: A review. Informatics, 11(4), 71. https://doi.org/10.3390/informatics11040071
- Ceselli, A., Premoli, M., & Secci, S. (2017). Mobile edge cloud network design optimization. IEEE/ACM Transactions on Networking, 25(3), 1818-1831. https://doi.org/10.1109/TNET.2017.2652850
- El-Sayed, H., Sankar, S., Prasad, M., Puthal, D., Gupta, A., Mohanty, M., & Lin, C. T. (2017). Edge of things: The big picture on the integration of edge, IoT and the cloud in a distributed computing environment. IEEE Access, 6, 1706-1717. https://doi.org/10.1109/ACCESS.2017.2780087
- Gunantara, N. (2018). A review of multi-objective optimization: Methods and its applications. Cogent Engineering, 5(1), 1-16. https://doi.org/10.1080/23311916.2018.1502242
- Jia, M., Cao, J., & Liang, W. (2015). Optimal cloudlet placement and user to cloudlet allocation in wireless metropolitan area networks. IEEE Transactions on Cloud Computing, 5(4), 725-737. https://doi.org/10.1109/TCC.2015.2449834
- Madni, S. H. H., Latiff, M. S. A., Ali, J., & Abdulhamid, S. I. M. (2019). Multi-objective-oriented cuckoo search optimization-based resource scheduling algorithm for clouds. Arabian Journal for Science and Engineering, 44(4), 3585-3602. https://doi.org/10.1007/s13369-018-3602-7
- Malik, S. U., Akram, H., Gill, S. S., Pervaiz, H., & Malik, H. (2021). EFFORT: Energy efficient framework for offload communication in mobile cloud computing. Software: Practice and Experience, 51(9), 1896-1909. https://doi.org/10.1002/spe.2850
- Mukherjee, A., De, D., & Buyya, R. (2024). Cloud Computing Resource Management. In: A. Mukherjee, D. De, R. Buyya, R. (Eds) Resource Management in Distributed Systems. Studies in Big Data (pp. 17-37). https://doi.org/10.1007/978-981-97-2644-8_2
- Mukherjee, A., De, D., Ghosh, S. K., & Buyya, R. (2021). Introduction to mobile edge computing. In A. Mukherjee, D. De, S.K. Ghosh, R. Buyya. (Eds) Mobile Edge Computing (pp. 3-19). https://doi.org/10.1007/978-3-030-69893-5_1
- Multi-objective optimization with Pareto front. https://www.d3view.com/multi-objective-optimization-with-pareto-front/ Accessed 7 October 2025
- Nandal, P., Bura, D., Singh, M., & Kumar, S. (2021). Analysis of different load balancing algorithms in cloud computing. International Journal of Cloud Applications and Computing (IJCAC), 11(4), 100-112. https://doi.org/10.4018/IJCAC.2021100106
- Nayyer, M. Z., Raza, I., & Hussain, S. A. (2018). A survey of cloudlet-based mobile augmentation approaches for resource optimization. ACM Computing Surveys (CSUR), 51(5), 1-28. https://doi.org/10.1145/3241738
- Rajak, N., & Shukla, D. (2018). Comparative study of cloud computing and mobile cloud computing. International Journal of Engineering Sciences & Research Technology (IJESRT), 7(3), 734-739.
- Sharma, M., & Prachi, B. (2017). Study On Mobile Cloud Computing, It's Architecture, Challenges and Various Trends. International Research Journal of Engineering and Technology (IRJET), 4(6), 1168-1177.
- Shen, C., Xue, S. & Fu, S. (2019). ECPM: an energy-efficient cloudlet placement method in mobile cloud environment. EURASIP Journal on Wireless Communications and Networking 2019, 141. https://doi.org/10.1186/s13638-019-1455-8
- Somula, R. S., & Sasikala, R. (2018). A survey on mobile cloud computing: mobile computing+ cloud computing (MCC= MC+ CC). Scalable Computing: Practice and Experience, 19(4), 309-337. https://doi.org/10.12694/scpe.v19i4.1411
- Tian, L., & Zhong, X. (2022). A case study of edge computing implementations: Multi-access edge computing, fog computing and cloudlet. Journal of Computing and Information Technology, 30(3), 139-159. https://doi.org/10.20532/cit.2022.1005646
- Tsai, Y., Chang, D., & Hsu, T. (2022). Edge Computing Based on Federated Learning for Machine Monitoring. Applied Sciences, 12(10), 5178. https://doi.org/10.3390/app12105178
- Yuyi, M., You, C., Zhang, J., Huang, K., & Letaief, K. (2017). A survey on mobile edge computing: The communication perspective. IEEE Communications Surveys & Tutorials, 19(4), 2322-2358. https://doi.org/10.1109/COMST.2017.2745201
- Zhao, M., & Zhou, K. (2019). Selective Offloading by Exploiting ARIMA-BP for Energy Optimization in Mobile Edge Computing Networks. Algorithms, 12(2), 48. https://doi.org/10.3390/a12020048
