Abstract: In this article, a new Multi-Layered mamdani fuzzy inference system (ML-MFIS) is propound for the Assessment of Efficient Load Balancing (ELB). The proposed ELB-ML-MFIS expert System can categorise the level of ELB in Cloud computing into Excellent, Normal or Low. ELB-ML-MFIS Expert System for ELB in cloud computing is developed under the guidelines from the Microsoft Organization and Pakistan’s Punjab Information Technology Board (PITB) Standard. ELB-ML-MFIS Expert System uses input Cloud Computing parameters such as Data-Center, Virtual-Machine, and Inter –of-Things (IOT) for different layers. This article also analyses the intensities of the Parametres and the results achieved by using the Proposed ELB-ML-MFIS Expert System. All these parameters and results are discussed with the experts of Pakistan’s Punjab Information Technology Board (PITB), Lahore. The accuracy of the proposed ELB-ML-MFIS Expert System is more accurate as compared to other approaches used for it.

Naila Samar Naz, Sagheer Abbas, Muhammad Adnan Khan, Benish Abid, Nadeem Tariq and Muhammad Farrukh Khan, “Efficient Load Balancing in Cloud Computing using Multi-Layered Mamdani Fuzzy Inference Expert System” International Journal of Advanced Computer Science and Applications(IJACSA), 10(3), 2019. http://dx.doi.org/10.14569/IJACSA.2019.0100373

@article{Naz2019,
title = {Efficient Load Balancing in Cloud Computing using Multi-Layered Mamdani Fuzzy Inference Expert System},
journal = {International Journal of Advanced Computer Science and Applications}
doi = {10.14569/IJACSA.2019.0100373},
url = {http://dx.doi.org/10.14569/IJACSA.2019.0100373},
year = {2019},
publisher = {The Science and Information Organization},
volume = {10},
number = {3},
author = {Naila Samar Naz and Sagheer Abbas and Muhammad Adnan Khan and Benish Abid and Nadeem Tariq and Muhammad Farrukh Khan},
}