SDN-Based Network Slicing Mechanism for a Scalable 4G/5G Core Network: A Kubernetes Approach †
Abstract
:1. Introduction
- Enhanced mobile broadband (eMBB) will provide higher bandwidths and lower latency. It will be suitable for smart home applications, ultra-high-definition (UHD) television, or Cloud gaming services (for example, Google Stadia).
- Massive machine-type communications (mMTC) will improve the existing low-power wide-area networks (LPWANs). The purpose of this type of service is to pave the way for smart cities. The two LPWAN technologies used in 4G networks, NB-IoT and LTE-M, will still be used and will be important factors in meeting the requirements of the 5G standard for IoT.
- Ultra-reliable and low-latency communications (URLLC) is a special type of communications designed for mission-critical applications which require more bandwidth and lower latencies than mMTC. A specific case is illustrated in [8], where URLLC slices provide low latency for autonomous cars in an SDN-based core network (CN).
2. Related Work
3. Proposed System Architecture
- The first experiment is related to a containerized telemetry application for IoT devices. We aim to analyze and compare the resource consumption of this application in three different deployment scenarios for both cloud and edge: (1) using a balenaCloud environment, (2) using AWS EC2 instances, and (3) using the AWS IoT cloud service. We chose these cloud environments because they can be used in a wide variety of deployment scenarios for IoT and M2M systems. balenaCloud is suitable for building and deploying containerized applications on remote devices in the edge. In AWS, we implemented the same application to perform a comparison in terms of resource consumption between cloud and edge. Third, Amazon Web Services IoT is one of the five major solutions with the largest market share alongside equivalent IoT platforms from Microsoft, Cisco, Google, and IBM [39]. Moreover, AWS IoT can be further integrated with AWS Wavelength for 5G deployments.
- The second experiment focuses on implementing an end-to-end testbed in order to deliver different types of traffic, with different traffic requirements through specific network slices when network congestion is detected. Moreover, the Mobile Private Cloud (MPC) network components are deployed as CNFs and orchestrated with Kubernetes. The motivation for using Kubernetes was to be able to provide high availability, scalability based on latency, and lifecycle management for the CNFs. This scenario aims to prove the feasibility and efficiency of our proposed algorithm for scaling the UPF and load-balancing the network traffic to the least-load CNF.
3.1. First Experiment: Monitoring the Resource Consumption of an IoT Telemetry Application in balenaCloud, Amazon Web Services, and Amazon Web Services IoT
3.1.1. balenaCloud
3.1.2. Amazon Web Services
3.1.3. Amazon Web Services IoT
3.2. Second Experiment: Network Slice Scalability Managed with SDN and Kubernetes in a Private Cloud Orchestrated by OpenStack
3.2.1. Testbed Description
3.2.2. Migration to Cloud-Native Network Functions
3.2.3. Scaling Algorithm for UPF
3.2.4. Network Slicing for Different Types of Network Traffic
4. Experimental Results
4.1. Experimental Results for the First Scenario
4.2. Experimental Results for the Second Scenario
5. Discussions and Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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M2M | IoT |
---|---|
Point-to-point communications | Connectivity via IP networks |
Hardware-based technology | Suitable for both hardware and software |
Not dependent on the Internet | Relies on Internet connectivity |
Device-based communications | Interface devices with gateways or data systems |
Limited scalability | Scalability is a key requirement |
Scope | LTE Band | Duplex Scheme | UL Carrier Frequency | DL Carrier Frequency | Channel Bandwidth | Carrier Power | MAC Address |
---|---|---|---|---|---|---|---|
IoT | 3 | FDD | 1720 MHz | 1815 MHz | 10 MHz | 24 dBm | D6:EF:CD:89:53:0B |
MBB | 38 | TDD | 2610 MHz | 2610 MHz | 20 MHz | 24 dBm | D6:EF:CD:88:EF:E4 |
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Botez, R.; Costa-Requena, J.; Ivanciu, I.-A.; Strautiu, V.; Dobrota, V. SDN-Based Network Slicing Mechanism for a Scalable 4G/5G Core Network: A Kubernetes Approach. Sensors 2021, 21, 3773. https://doi.org/10.3390/s21113773
Botez R, Costa-Requena J, Ivanciu I-A, Strautiu V, Dobrota V. SDN-Based Network Slicing Mechanism for a Scalable 4G/5G Core Network: A Kubernetes Approach. Sensors. 2021; 21(11):3773. https://doi.org/10.3390/s21113773
Chicago/Turabian StyleBotez, Robert, Jose Costa-Requena, Iustin-Alexandru Ivanciu, Vlad Strautiu, and Virgil Dobrota. 2021. "SDN-Based Network Slicing Mechanism for a Scalable 4G/5G Core Network: A Kubernetes Approach" Sensors 21, no. 11: 3773. https://doi.org/10.3390/s21113773
APA StyleBotez, R., Costa-Requena, J., Ivanciu, I.-A., Strautiu, V., & Dobrota, V. (2021). SDN-Based Network Slicing Mechanism for a Scalable 4G/5G Core Network: A Kubernetes Approach. Sensors, 21(11), 3773. https://doi.org/10.3390/s21113773