KLASIFIKASI PEMANFAATAN OPEN SOURCE RESOURCE PADA MULTI-EDGE COMPUTING DENGAN JARINGAN PRIVATE 5G YANG BERKUALITAS DAN LOW LATENCY MENGGUNAKAN TEKNOLOGI NETWORK SLICING
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Multi-edge computing is one of the technologies that allows data processing to be performed at the edge of the network, thereby reducing latency and speeding up overall data processing. On the other hand, 5G private networks with high quality and low latency enable fast and stable data transmission. However, in implementing multi-edge computing technology with 5G private networks, it is necessary to use the right open source resources to support its success at a lower investment cost. This thesis aims to classify the utilization of open source resources in multi-edge computing technology with quality and low latency 5G private networks using network slicing technology. The development methodology includes literature study, system development, data collection, data analysis, and system performance evaluation implemented on top of type 2 hypervisor virtualization on Virtualbox. The results of this thesis are expected to show that there are several open source resources that can be used in multi-edge computing technology with 5G private networks, such as Open5gs, Kubernetes, Virtualbox, so as to form a network with low latency and low cost.
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Program Studi Teknik Informatika, Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika, Kota Jakarta Timur, Daerah Khusus Ibukota Jakarta, Indonesia
Program Studi Teknik Informatika, Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika, Kota Jakarta Timur, Daerah Khusus Ibukota Jakarta, Indonesia
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References
Total: 20 References- Hassan, N., Yau, K. L. A., & Wu, C. (2019). Edge computing in 5G: A review. IEEE Access, 7, 127276-127289. DOI: 10.1109/ACCESS.2019.2938534.
- Joshi, S., Rashidi-Tabrizi, A., Kour, S., Abuibaid, M., & Huang, J. S. (2022, October). 5G Deployment and Simulation Using Firecracker for Medical Application. In International Conference on Computer Engineering and Networks (pp. 1250-1259). Singapore: Springer Nature Singapore. DOI: 10.1007/978-981-19-6901-0_131.
- Kourtis, M. A., Sarlas, T., Anagnostopoulos, T., Kukliński, S., Tomaszewski, L., Wierzbicki, M., ... & Koumaras, H. (2021). Network slicing for 5G edge services. Internet Technology Letters, 4(6), e289. DOI: 10.1002/itl2.289.
- Barakabitze, A. A., Ahmad, A., Mijumbi, R., & Hines, A. (2020). 5G network slicing using SDN and NFV: A survey of taxonomy, architectures and future challenges. Computer Networks, 167, 106984.
- Shorov, A. (2019, January). 5G testbed development for network slicing evaluation. In 2019 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus) (pp. 39-44). IEEE. DOI: 10.1109/EIConRus.2019.8656861.
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