Exploiting a Cloud Framework for Automatically and Effectively Providing Data Analyzers

Abstract

Recently big data are crucial important for data computing and analytics. Traditional computing paradigm is inefficient for computing by the complexity and computational cost. Cloud computing is a modern trend of computing paradigm in which typically real-time scalable resources such as files, data, programs, hardware, and third party services can be accessible from a web browser via the Internet to users. It is the new trend for big data analytics that provides high reliability, availability, and scalability services. This paper proposed an automated cloud analysis framework and management system based on OpenStack and other open-source projects such as Apache Spark, Sparkler, RESTful API, and JBoss web server. The automated cloud provides a cluster of virtual machines which utilizes the storage and memory in order to support multiple data analysis. In addition, OpenStack also provide services for authenticating and user account management on cloud environment which enhance the cloud security. In addition, REST provides a set of architectural constraints that, when applied as a whole, emphasizes scalability of component interactions, generality of interfaces, independent deployment of components, and intermediary components to reduce interaction latency, enforce security, and encapsulate legacy systems. RESTful API is the essential implementation of REST web architecture for web services. It provide data and services are shared on cloud through uniform interface. Finally, data analysis works effectively by using parallel computing model with realtime data processing in Apache Spark and Sparkler.