Efficient Load Balancing in Cloud Computing using Multi-Layered Mamdani Fuzzy Inference Expert System

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.

@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},
  volume    = {10},
  number    = {3},
  year      = {2019},
  publisher = {The Science and Information Organization},
  author    = {Naila Samar Naz and Sagheer Abbas and Muhammad Adnan Khan and Benish Abid and Nadeem Tariq and Muhammad Farrukh Khan},
  doi       = {10.14569/IJACSA.2019.0100373},
  url       = {https://doi.org/10.14569/IJACSA.2019.0100373}
}