Real time Anomaly Detection in Network KPI using Time Series

Abstract:

How to accurately detect Key Performance Indicator (KPI) anomalies is a critical issue in cellular network management. In this talk I shall introduce CNR(Cellular Network Regression) a unified performance anomaly detection framework for KPI time-series data. CNR realizes simple statistical modelling and machine-learning-based regression for anomaly detection; in particular, it specifically takes into account seasonality and trend components as well as supports automated prediction model retraining based on prior detection results. I demonstrate here how CNR detects two types of anomalies of practical interest, namely sudden drops and correlation changes, based on a large-scale real-world KPI dataset collected from a metropolitan LTE network. I explore various prediction algorithms and feature selection strategies, and provide insights into how regression analysis can make automated and accurate KPI anomaly detection viable.

Index Terms—anomaly detection, NPAR (Network Performance Analysis)

  1. INTRODUCTION

The continuing advances of cellular network technologies make high-speed mobile Internet access a norm. However, cellular networks are large and complex by nature, and hence production cellular networks often suffer from performance degradations or failures due to various reasons, such as back- ground interference, power outages, malfunctions of network elements, and cable disconnections. It is thus critical for network administrators to detect and respond to performance anomalies of cellular networks in real time, so as to maintain network dependability and improve subscriber service quality. To pinpoint performance issues in cellular networks, a common practice adopted by network administrators is to monitor a diverse set of Key Performance Indicators (KPIs), which provide time-series data measurements that quantify specific performance aspects of network elements and resource usage. The main task of network administrators is to identify any KPI anomalies, which refer to unexpected patterns that occur at a single time instant or over a prolonged time period.

Today’s network diagnosis still mostly relies on domain experts to manually configure anomaly detection rules such a practice is error-prone, labour intensive, and inflexible. Recent studies propose to use (supervised) machine learning for anomaly detection in cellular networks . ellular networks, a common practice adopted by network administrators is to monitor a diverse set of Key Performance Indicators (KPIs), which provide time-series data measurements that quantify specific performance aspects of network elements and resource usage. The main task of network administrators is to identify any KPI anomalies, which refer to unexpected patterns that occur at a single time instant or over a prolonged time period.

Today’s network diagnosis still mostly relies on domain experts to manually configure anomaly detection rules such a practice is error-prone, labour intensive, and inflexible. Recent studies propose to use (supervised) machine learning for anomaly detection in cellular networks .

 
 

Outline/Structure of the Experience Report

Introduction: Discussion on the problem Statement

Objective: Discussion on the target that can be achieved

Sustainability and Future Scope

Real time Anomaly Detection

Learning Outcome

The audience is expected to receive an overview on the following topic

1.Network Architecture

2.What is Network KPI and how it works

3.How Anomaly can be detected using different Machine Learning models

Target Audience

Anybody who has an inclination in Science and Technology

schedule Submitted 7 months ago

Public Feedback

comment Suggest improvements to the Speaker
  • Dr. Vikas Agrawal
    By Dr. Vikas Agrawal  ~  7 months ago
    reply Reply

    Dear Siboli: Have you applied any of these techniques on real-world data at your employer or elsewhere?Will that data or results be shared at ODSC?

    In addition, I wonder about the challenges with non-stationarity and non-linearities in real world time series, and multiple relationships between time series variables.

    Warm Regards

    Vikas

    • Siboli Mukherjee
      By Siboli Mukherjee  ~  6 months ago
      reply Reply

      Dear Vikas

      Presently i am working in this domain.My talk will be more theoretical presentation (but if time extends from 20 mins to 45 mins i can show this practically) where I shall explain Anomaly detection for Network KPI Indicators through Machine Learning, judging whether Network KPI(traffic ,call drop etc)is stable or not.

  • Anoop Kulkarni
    By Anoop Kulkarni  ~  7 months ago
    reply Reply

    Thanks for your proposal. Just curious, will this be a theoretical presentation or even include demo elements? If latter, what would be your dataset? What parameters you would try to train ML on? What ML algorithms would be more interesting?

    Would be good if you could include these points in the proposal and maybe make the overall proposal more concise and shorter.

     

    Thanks

    ~anoop

    • Siboli Mukherjee
      By Siboli Mukherjee  ~  7 months ago
      reply Reply

      Thank you for your Suggestion.Well ,my talk will be more theoretical presentation where I shall explain Anomaly detection for Network KPI Indicators through Machine Learning, judging whether Network KPI(traffic ,call drop etc)is stable or not.I want to use the statistical and machine learning methods to detect anomalous.I shall split it into two parts a) I use Time Series analysis Method such as Triple Order Exponential Smoothing(Holt Winters) and ARIMA model and regression based machine learning techniques such as Gradiant Boosting Regression trees (GBRT)and Long short Term memory (LSTM)to predict the value at the next time in the time series, after that anomaly detection rule will be set.Finally I compare the predicted value  with actual value to determine whether the current point is anomalous or not.


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    “Alexa, launch Netflix!”

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