Peak Hour Mobile Core Network Data Traffic Analysis to Improve Network Quality Using Flow Based Method: The Case of Ethio-Telecom
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Date
2021-10
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Addis Ababa University
Abstract
It is known that the telecom industry is one of the core areas in a country's sustainability and
growth. So it is important that great emphasis be given to it on deploying necessary
infrastructures in different areas, maintaining the existing available resources and also upgrading
the already existing networks as necessary. Once the basic layout is done, it is also equally
important that the necessary follow up is done for giving solutions to problems that arise from
customers from time to time. One of the biggest reason that lead to customer complaints arise
from poor quality of service which results in dissatisfaction of customers' needs. In order to give
a solution to this, one of the ways is to do a network traffic analysis.
In this thesis, a data traffic analysis is done in the Ethio Telecom core network. Data captured
from its network is used as an input in order to firstly identify the peak hour during the day
because this is the time where there is the most communication and transmission. The peak hours
of each day are recoded and then finally the average is taken for the purpose of this study. In
general over the sampled data the peak hour is found to be at 21:06hr. For this work
identification of the peak hour is necessary because this thesis focuses the traffic analysis during
the peak hour and for the work to be thorough and to be confirmed, first identification of the
busiest hour of the day is necessary. After that by filtering out the data at the peak hour, the Key
Performance Indicators, Packet Loss Ratio in percentage (%) and throughput (packet/sec) are
studied from the capture data in order to be able to see how exactly the system is working. In
order to do so, two approaches are used. First the cumulative distribution functions of the data
are fitted against the different traffic analysis distribution models. Out of the selected distribution
models, it is seen that our data best fits with the Normal Distribution and the Gamma
Distributions. For better accuracy the RMSE (Root Mean Square Error) is calculated for each
one of them. Second, the KPI's for the peak hour and the slow hour are compared. From the
sample gathered data, for both PLR and throughputs, the number of packets being lost are higher
during the peak hour compared to that of the slow hour by 37%. But despite this, when
comparing the Packet Loss Ratio recorded for both peak hour and slow hour they are both less
than 1% which is the acceptable threshold range.
Similarly the number of packets being received per second that are sent for the downlink and
uplink throughputs, during peak hour the minimum downlink and uplink throughputs exceed that of the slow hour by 15.4% and 11.9% respectively and for the uplink throughput by 16% and
12.5% respectively. So finally from the analysis result, it is seen that the network works fine with
a very minor glitch which is expected from a real life operating network.
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Keywords
Traffic analysis, peak hour, KPI's, PLR, throughput, RMSE