Quality of Monitoring for Cellular Networks

Naser Hossein Motlagh,Rola Alhalaseh,Shubham Kapoor,Sasu Tarkoma,Kimmo Hatonen

doi:10.1109/tnsm.2021.3112467

Naser Hossein Motlagh, Rola Alhalaseh + Show 3 more

Open Access

https://doi.org/10.1109/tnsm.2021.3112467

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Abstract

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 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Exact , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Optimized , and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Sharp . For all KPIs, the class <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Optimized has a higher compression rate than the class <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Exact , while the class <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">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.

Highlights

The evolution of the 5th Generation of Mobile Networks (5G) and beyond is accompanied by an increase in the number of end devices and network services [1]. 5G networks are expected to have more distributed Network Elements (NEs) to meet the demand of ultra-low latency and high throughput to the end-users [2]
When the Quality of Monitoring (QoM) was not implemented, the CPU utilization was higher and the available memory decreased with the increase in Key Performance Indicators (KPIs) subscriptions
We presented the concept of Quality of Monitoring (QoM) as a solution for monitoring the M-Plane data which aggregates the data into KPIs and uses a set of classes to compress data at the mobile edge before monitoring

Summary

Introduction

The evolution of the 5th Generation of Mobile Networks (5G) and beyond is accompanied by an increase in the number of end devices and network services [1]. 5G networks are expected to have more distributed NEs (e.g., small cells) to meet the demand of ultra-low latency and high throughput to the end-users [2]. Network providers are compelled to integrate the new NEs into existing infrastructure instead of replacing the old ones This increases the complexity and heterogeneity of the mobile networks leading to challenges in network management and operation [3]. The multiplication of NEs and the complexity of networks will increase the volume of data sourced from the NEs; and brings up the data transfer, data storage, and data aggregation challenges at the network Management-Plane (MPlane). To operate these complex mobile networks in an optimal state, fast collection of network statistics from the MPlane enables monitoring and management of the networks in real-time. It is worth noting that the “M-Plane” refers to functions, interfaces, protocols, data formats, and storage, which are used by NMSs and applications to control, configure and monitor the network status [5], [6]

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