The increasing demand for cloud computing has shifted business toward a huge demand for cloud services, which offer platform, software, and infrastructure for the day-to-day use of cloud consumers. Numerous new cloud service providers have been introduced to the market with unique features that assist service developers collaborate and migrate services among multiple cloud service providers to address the varying requirements of cloud consumers. Many interfaces and proprietary application programming interfaces (API) are available for migration and collaboration services among cloud providers, but lack standardization efforts. The target of the research work was to summarize the issues involved in semantic cloud portability and interoperability in the multi-cloud environment and define the standardization effort imminently needed for migrating and collaborating services in the multi-cloud environment.

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.

Enterprise Service Bus is an infrastructure to facilitate Service Oriented Architecture (SOA). SOA has gained a lot of attention over the most recent years and has become the de-facto standard for web application and software component integration. Web services are the prominent model for interoperable applications across heterogeneous systems and electronic business which use SOA and it has been used in various applications. The web services available on the web is increasing day by day, hence web service discovery is becoming a difficult and time consuming task. To discover services, clustering web services is an efficient approach. It is also necessary to compose several web services in order to achieve the user's goal. The chapter presents the background of web services and the various data mining techniques used for clustering web services. The chapter presents the various web services clustering method and the related work that discusses the various techniques to cluster the web services will also be addressed.

Semantic interoperability (SI) is defined as the capability of interpreting the nature of the information exchanged inside cloud computing (CC). For SI, ontology is selected as a solution. A hierarchical structure is offered by an ontology that comprises semantic relations between the application and the cloud server (CS). Even though different methods are introduced, ontology centered on semantic relation between the application and CS is not yet attained for achieving accurate interoperability. An ontology‐centered SI in CC utilizing a modified Shell Game optimization‐centered recurrent neural network (MSGO‐RNN) is presented in this work. Here, accessing the data from the data warehouse is initiated that experiences different standardization functions for improving the data’s quality, namely, data extraction, data cleaning, data transformation, data loading, and refreshing. Then, the data are passed via the semantic layer (SL), which offers a constant way of interpreting data. Then, for enhancing the accuracy rate of interoperability along with offering a low computational time, the relevant feature is chosen utilizing genetic crossover mutation‐centered improved fertile field (GCM‐IFF) optimization. Ontology is created utilizing the Protégé tool centered on the data, and respective manipulation is executed utilizing HermiT ontology web language (OWL) reasoner. Lastly, to attain cloud interoperability, the features are trained and tested by employing the MSGO‐RNN. The experimental outcome exhibits that when compared with the top‐notch methods, an accuracy of 96.32% is acquired by the framework for choosing the CS, and less time of 22567 ms is acquired by the framework for training.