An Efficient Applications Cloud Interoperability Framework Using I-Anfis

• Main Authors: Chithambaramani Ramalingam, Prakash Mohan
• Format: Article
• Language: English
• Published: MDPI AG 2021-02-01
• Series: Symmetry
• Subjects: hybrid squirrel search genetic (HSSG) algorithm; improved adaptive neuro-fuzzy inference system (I-ANFIS)
• Online Access: https://www.mdpi.com/2073-8994/13/2/268

Cloud interoperability provides cloud services such as Software as a Service (SaaS) or customer system to communicate between the cloud providers. However, one of the most important barriers for existing researches was to adopt the application’s or data’s in cloud computing environments so as to obtain efficient cloud interoperability. This paper focuses on reliable cloud interoperability with a heterogeneous cloud computing resource environment with the objective of providing unilateral provision computing capabilities of a cloud server without the help of human interaction and allowing proper utilization of applications and services across various domains by using an effective cloud environment available at runtime. Moreover, the framework uses hybrid squirrel search genetic algorithm (HSSGA) to select the relevant features from a set of extracted features in order to eliminate irrelevant data which provides advantages of low computational time and less memory usage. Thereafter, for a proper selection of cloud server with respect to the selected features, the system has developed the improved adaptive neuro-fuzzy inference system (I-ANFIS) which provides accurate server selection and helps against uncertainties caused by servers or applications. Hence, the experimental result of the proposed framework gives an accuracy of 94.24% and remains more efficient compared to existing frameworks.