SDRBench: A Software-Defined Radio Access Network Controller Benchmark

Papa, Arled; Durner, Raphael; Edinger, Fabian; Kellerer, Wolfgang

doi:10.1109/netsoft.2019.8806697

Cited by 4 publications

(5 citation statements)

References 14 publications

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“…Intuitively, the signaling overhead is affected by the number of slices, users of a slice and gNBs that the RANCF is controlling as well as the size and frequency of control message updates. Initial benchmark of FlexRAN SD-RAN controller is provided in [14], where the impact of controlling multiple gNBs is presented with respect to memory, CPU consumption and slice initiation time. Even though users are not present in the current version of the benchmarking tool, an insight of signaling overhead is provided for the impact of the underlying network on SD-RAN controllers.…”

Section: Discussionmentioning

confidence: 99%

Controlling Next-Generation Software-Defined RANs

et al. 2020

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Compared to previous mobile generations, the 5G system architecture offers more flexibility to fulfill a richer set of diverse requirements. Three system aspects play a major role here: support of network slicing, a split between the user plane and the control plane, and a service oriented control architecture. Emerging techniques of network softwarization such as Software-Defined Networking (SDN) are leveraged to provide this notion of flexibility manifested in a Software-Defined core network. However, in addition to the 5G Core (5GC), analogous concepts could be applied to the Next Generation Radio Access Network (NG-RAN) to sustain the required system performance. Driven by the increased attention that is currently paid to a Software-Defined Radio Access Network (SD-RAN), in this article, we propose the Radio Access Network Control Function (RANCF) as an additional 5G control function. We define the main functionalities of RANCF and propose interfaces and protocols for the interaction with 5GC functions. Exploiting network slicing as an example, we demonstrate how RANCF facilitates the slice selection and slice management procedure.

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Section: Discussionmentioning

confidence: 99%

Controlling Next-Generation Software-Defined RANs

et al. 2020

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“…The RAN runtime module is in an abstraction layer that is different from RTC but has a hierarchical binding under RTC. Each RAN runtime module will act separately from one another and has the respective communication facilities with RTC through the RAN agent contained in each RAN runtime module (Papa et al, 2019). On the other hand, the Application Plane has a mapping that connects each RAN runtime module with the Software Development Kit (SDK).…”

Section: Flexranmentioning

confidence: 99%

Performance Evaluation of Anomaly Detection System on Portable LTE Telecommunication Networks Using OpenAirInterface and ELK

Nugroho¹,

Harwahyu²,

Sari³

et al. 2023

IJTech

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Anomaly detection (AD) is a solution for cellular operators to overcome the difficulty of quality control over the proliferation of cellular phone usage. The telecommunications network monitoring system with anomaly detection enables immediate discovery of problems before they become more complex. Monitoring activities produce logs, which are then analyzed according to the interest, often with the help of statistics and visualizations. Relying on humans for analysis is increasingly difficult due to the immense amount of logs in modern telecommunication networks. This work extends the monitoring to automated anomaly detection by using various ELK modules to form an intelligent monitoring system. A testbed based on OpenAirInterface (OAI) and USRP B210 radiohead is used, which includes the functionalities of HSS, MME, SGW, PGW, eNB, and UE. The proposed system has an average accuracy of 91.5%. This is supported by an average value of the proportion of normal conditions that are correctly predicted at 99.31%. On the other hand, the system can still maintain the functionality of the cellular telecommunications network with an excellent predicate on service quality.

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“…This enables faster progress towards satisfying the new 5G requirements [2], and at the same time triggers a closer collaboration between industry and academia. However, while the current evaluation provided for 5G-EmPOWER [9], Orion [32], OpenAirInterface, srsLTE in [33] and FlexRAN [8], [34] is sufficient for academic purposes, the possibility for a commercial use requires further investigation with focus on performance guarantees in realistic network dimensions, which is currently limited.…”

Section: Related Workmentioning

confidence: 99%

“…However, only 4 Base Stations (BSs) and 16 emulated UEs are considered and thus the real scalability of the controller remains unknown. In [34] an evaluation is provided for the CPU and memory utilization of FlexRAN with up to 250 emulated BSs. Nonetheless, no UEs are considered, thus an important source of signaling overhead is neglected.…”

Section: Related Workmentioning

confidence: 99%

MARC: On Modeling and Analysis of Software-Defined Radio Access Network Controllers

Papa

Durner

Goshi

et al. 2021

IEEE Trans. Netw. Serv. Manage.

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Network programmability also sneaked into the mobile world leading to the emergence of Software-Defined Radio Access Network (SD-RAN) architectures. Interestingly, while only a small number of prototype architectures exist for SD-RAN, their performance evaluations are unfortunately also limited. Recent evaluations are carried out for small network dimensions of up to 50 devices, while emerging 5G/6G networks envision numbers of devices beyond 5000. Although 5G/6G applications are more stringent with respect to latency guarantees, performance evaluations of such low scale remain questionable. To fill this void, this paper presents MARC: a novel benchmarking tool for SD-RAN architectures and their controllers. We use MARC to measure, analyze and identify performance implications for two state-of-the-art open source SD-RAN solutions: FlexRAN and 5G-EmPOWER. We perceive results for monitoring application scenarios considering fully centralized control. For this setting, our findings show that the proposed architectures with a single SD-RAN controller are not scalable and can even lead to unpredictable network operations. Using our tool and based on our insights, we provide and implement design guidelines for the internal working behavior of the existing controllers.

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SDRBench: A Software-Defined Radio Access Network Controller Benchmark

Cited by 4 publications

References 14 publications

Controlling Next-Generation Software-Defined RANs

Controlling Next-Generation Software-Defined RANs

Performance Evaluation of Anomaly Detection System on Portable LTE Telecommunication Networks Using OpenAirInterface and ELK

MARC: On Modeling and Analysis of Software-Defined Radio Access Network Controllers

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