Anand Padmanabha Iyer and Ion Stoica and Mosharaf Chowdhury and Li Erran Li

EECS Department, University of California, Berkeley

Technical Report No. UCB/EECS-2016-114

May 17, 2016

http://www2.eecs.berkeley.edu/Pubs/TechRpts/2016/EECS-2016-114.pdf

An increasing amount of analytics is performed on data that is procured in a real-time fashion to make real-time decisions. Such tasks include simple reporting on streams to sophisticated model building. However, the practicality of such analyses are impeded in several domains because they are faced with a fundamental trade-off between data collection latency and analysis accuracy.

In this paper, we study this trade-off in the context of a specific domain, Cellular Radio Access Networks (RAN). Our choice of this domain is influenced by its commonalities with several other domains that produce real-time data, our access to a large live dataset, and their real-time nature and dimensionality which makes it a natural fit for a popular analysis technique, machine learning (ML). We find that the latency accuracy trade-off can be resolved using two broad, general techniques: intelligent data grouping and task formulations that leverage domain characteristics. Based on this, we present CellScope, a system that addresses this challenge by applying a domain specific formulation and application of Multi-task Learning (MTL) to RAN performance analysis. It achieves this goal using three techniques: feature engineering to transform raw data into effective features, a PCA inspired similarity metric to group data from geographically nearby base stations sharing performance commonalities, and a hybrid online-offline model for efficient model updates. Our evaluation of CellScope shows that its accuracy improvements over direct application of ML range from 2.5× to 4.4× while reducing the model update overhead by up to 4.8×. We have also used CellScope to analyze a live LTE consisting of over 2 million subscribers for a period of over 10 months, where it uncovered several problems and insights, some of them previously unknown.

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BibTeX citation:

@techreport{Padmanabha Iyer:EECS-2016-114,
    Author= {Padmanabha Iyer, Anand and Stoica, Ion and Chowdhury, Mosharaf and Erran Li, Li},
    Title= {Fast and Accurate Performance Analysis of LTE Radio Access Networks},
    Year= {2016},
    Month= {May},
    Url= {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2016/EECS-2016-114.html},
    Number= {UCB/EECS-2016-114},
    Abstract= {An increasing amount of analytics is performed on data that is procured in a real-time fashion to make real-time decisions. Such tasks include simple reporting on streams to sophisticated model building. However, the practicality of such analyses are impeded in several domains because they are faced with a fundamental trade-off between data collection latency and analysis accuracy.

In this paper, we study this trade-off in the context of a specific domain, Cellular Radio Access Networks (RAN). Our choice of this domain is influenced by its commonalities with several other domains that produce real-time data, our access to a large live dataset, and their real-time nature and dimensionality which makes it a natural fit for a popular analysis technique, machine learning (ML). We find that the latency accuracy trade-off can be resolved using two broad, general techniques: intelligent data grouping and task formulations that leverage domain characteristics. Based on this, we present CellScope, a system that addresses this challenge by applying a domain specific formulation and application of Multi-task Learning (MTL) to RAN performance analysis. It achieves this goal using three techniques: feature engineering to transform raw data into effective features, a PCA inspired similarity metric to group data from geographically nearby base stations sharing performance commonalities, and a hybrid online-offline model for efficient model updates. Our evaluation of CellScope shows that its accuracy improvements over direct application of ML range from 2.5× to 4.4× while reducing the model update overhead by up to 4.8×. We have also used CellScope to analyze a live LTE consisting of over 2 million subscribers for a period of over 10 months, where it uncovered several problems and insights, some of them previously unknown.},
}

EndNote citation:

%0 Report
%A Padmanabha Iyer, Anand 
%A Stoica, Ion 
%A Chowdhury, Mosharaf 
%A Erran Li, Li 
%T Fast and Accurate Performance Analysis of LTE Radio Access Networks
%I EECS Department, University of California, Berkeley
%D 2016
%8 May 17
%@ UCB/EECS-2016-114
%U http://www2.eecs.berkeley.edu/Pubs/TechRpts/2016/EECS-2016-114.html
%F Padmanabha Iyer:EECS-2016-114