ANFIS based Classification Model for Network Device Migration towards SoDIP6 Networks

Financially sustainable solution is the need to migrate legacy IPv4 network infrastructure into latest networking paradigms viz. Software-defined networking (SDN) and the Internet protocol version 6 (IPv6) addressing. For Internet service provider (ISP) networks, IP routers are either to be upgraded or replaced with new device to transform the network into Software-defined IPv6 (SoDIP6) networks. In this paper, we classify the network routers require its hardware/firmware either to be upgraded or replaced using adaptive neuro fuzzy inference system (ANFIS), a well-known intelligent approach used in classification, prediction, and estimation. Device specification details with end-of-life and end-of-support information are stored in the knowledge base (KB) system. Device performance parameters, for example average CPU usage, throughput, and memory capacity are being extracted in real time using simple network management protocol (SNMP) and mapped with information obtained from the KB for input to ANFIS. The experimental results show that proposed approach is more accurate and optimal that assists service providers for smooth transitioning to SoDIP6 networks with optimum cost of migration.

[1]  Shubair Abdulla.  A neuro-fuzzy system to detect IPv6 router alert option DoS packets , 2020, Int. Arab J. Inf. Technol..

[2]  Vladimir Trajkovik,et al.  An ANFIS model of quality of experience prediction in education , 2015, Appl. Soft Comput..

[3]  Hazlee Azil Illias,et al.  Short-Term Electricity Price Forecasting via Hybrid Backtracking Search Algorithm and ANFIS Approach , 2019, IEEE Access.

[4]  Amir H. Mohammadi,et al.  Predictive model based on ANFIS for estimation of thermal conductivity of carbon dioxide , 2016 .

[5]  Giovanni Mummolo,et al.  A FUZZY APPROACH FOR MEDICAL EQUIPMENT REPLACEMENT PLANNING , 2007 .

[6]  Chris Metz,et al.  Transition from IPv4 to IPv6: A State-of-the-Art Survey , 2013, IEEE Communications Surveys & Tutorials.

[7]  Pietro Manzoni,et al.  Legacy Network Integration with SDN-IP Implementation towards a Multi-Domain SoDIP6 Network Environment , 2020, Electronics.

[8]  Danda B. Rawat,et al.  Towards energy efficiency and green network infrastructure deployment in Nepal using software defined IPv6 network paradigm , 2019, Electron. J. Inf. Syst. Dev. Ctries..

[9]  Pietro Manzoni,et al.  Migration cost optimization for service provider legacy network migration to software‐defined IPv6 network , 2020, Int. J. Netw. Manag..

[10]  Danda B. Rawat,et al.  Software Defined IPv6 Network: A New Paradigm for Future Networking , 2019, Journal of the Institute of Engineering.

[11]  Vilas K. Patil,et al.  Prediction of L10 and Leq Noise Levels Due to Vehicular Traffic in Urban Area Using ANN and Adaptive Neuro-Fuzzy Interface System (ANFIS) Approach , 2019, Int. J. Bus. Data Commun. Netw..

[12]  Danda B. Rawat,et al.  Affordable Broadband with Software Defined IPv6 Network for Developing Rural Communities , 2019 .

[13]  Babu Ram Dawadi,et al.  Service Provider IPv4 to IPv6 Network Migration Strategies , 2015 .

[14]  Srete Nikolovski,et al.  A Novel ANFIS-Based Islanding Detection for Inverter-Interfaced Microgrids , 2019, IEEE Transactions on Smart Grid.

[15]  Diyar Akay,et al.  ANFIS and Deep Learning based missing sensor data prediction in IoT , 2019, Concurr. Comput. Pract. Exp..

[16]  Radek Martinek,et al.  The Real Implementation of ANFIS Channel Equalizer on the System of Software-Defined Radio , 2014 .

[17]  Hongnian Yu,et al.  A comparison of Mamdani and Sugeno fuzzy based packet scheduler for MANET with a realistic wireless propagation model , 2015, International Journal of Automation and Computing.

[18]  Nick McKeown,et al.  OpenFlow: enabling innovation in campus networks , 2008, CCRV.

[19]  Dervis Karaboga,et al.  Adaptive network based fuzzy inference system (ANFIS) training approaches: a comprehensive survey , 2018, Artificial Intelligence Review.

[20]  Mohamed Mohandes,et al.  Estimation of wind speed profile using adaptive neuro-fuzzy inference system (ANFIS) , 2011 .

[21]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..