Header menu link for other important links
Forecasting and event detection in internet resource dynamics using time series models
, S.V. Raghavan
Published in Newswood Ltd.
Volume: 23
Issue: 4
Pages: 245 - 257
At present, Internet emerges as a country’s predominant and viable data communication infrastructure. Autonomous System (AS) topology occupies the top position in the Internet infrastructure hierarchy. AS resources are building blocks of this topology, and consist of AS numbers, IPv4 and IPv6 prefixes. Further, the resource requirement in each country is dynamic and driven by various technical and socio-economic factors. Hence, the organizational and national competitiveness for socio economic development is reflected in AS growth pattern. Furthermore, to assess the competitiveness, future expansion, and policy development, there is a need for both study and forecast AS growth. For Internet infrastructure development, understanding long term trends and stochastic variation behaviour are essential to detecting significant events during growth periods. In this work, we use time series based approximation for mathematical modeling, system identification, and forecasting to determine the annual AS growth. The AS data of five countries, namely India, China, Japan, South Korea, and Taiwan were extracted from the APNIC (Asia Pacific Network Information Centre) archive for this purpose. The first two countries have larger economies and the next three countries are advanced technological nations in the APNIC region. The characterization of the time series is performed by analyzing the trend and fluctuation component of the data. The model identification is carried out by testing for non stationarity and autocorrelation significance. ARIMA (Auto Regressive Integrated Moving Average) models with different Auto Regressive (AR) and Moving Average (MA) parameters are identified for forecasting the AS growth of each country. Model validation, parameter estimation, point forecast, and prediction intervals with 95 % confidence levels for the five countries are reported in the paper. The statistical analysis on long term trends and Change Point Detection (CPD) on Inter Annual Absolute Variations (IAAV) are presented. The significant level of change in variations, positive growth percentage in IAAV, and higher percentage of advertised ASes when compared to other countries indicate India’s fast growth and wider global reachability of Internet infrastructure from 2007 onwards. The correlation between AS IAAV change points and GDP (gross domestic product) growth periods indicates that the service sector industry growth is the driving force behind significant annual changes. © International Association of Engineers.
About the journal
JournalEngineering Letters
PublisherNewswood Ltd.