Quality of monitoring for cellular networks
Motlagh, Naser Hossein; Kapoor, Shubham; Alhalaseh, Rola; Tarkoma, Sasu; Hätönen, Kimmo (2021-09-14)
N. Hossein Motlagh, S. Kapoor, R. Alhalaseh, S. Tarkoma and K. Hätönen, "Quality of Monitoring for Cellular Networks," in IEEE Transactions on Network and Service Management, vol. 19, no. 1, pp. 381-391, March 2022, doi: 10.1109/TNSM.2021.3112467
© The Author(s) 2022. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.
5G networks and beyond introduce a larger number of Network Elements (NEs) and functions than former cellular generations. The increase in NEs will, thus, result in significantly increasing the Management-Plane (M-Plane) data collected from the NEs. Therefore, the conventional centralized Network Management Systems (NMSs) will face fundamental challenges in processing the M-Plane data. In this paper, we present the concept of Quality of Monitoring (QoM) as a solution, which is able to reduce the M-Plane data already at the NEs. First, QoM aggregates the raw M-Plane data into Key Performance Indicators (KPIs). To these KPIs, the QoM applies a data-driven algorithm to define information loss limits for QoM classes specific for each KPI time series. Then, the QoM applies the classes for compressing the KPI data utilizing a lossy-compression method, which is a derivative of the Piece-Wise Constant Approximation (PWCA) algorithm. To evaluate the performance of the QoM solution, we use M-Plane raw data from a live LTE network and calculate four KPIs, while each KPI has different statistical characteristics. We also define three QoM classes named Exact, Optimized, and Sharp. For all KPIs, the class Optimized has a higher compression rate than the class Exact, while the class Sharp has the highest compression rate. Assuming that, for example, NEs of a network produce 280 MB of raw data containing information that needs to be transferred to the network operations center; we use KPIs to represent the information contents of the data, and QoM solution to transfer the data over the network. As a result, the QoM solution achieves an estimated 95% compression gain from the raw data in transfer.
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