Building a Productive Domain-Specific Cloud for Big Data Processing and Analytics Service

作     者：Yuzhong Yan Mahsa Hanifi Liqi Yi Lei Huang 

作者机构：Department of Computer Science Prairie View A&M University Prairie View TX USA Intel Corporation Hillsboro OR USA 

出 版 物：《Journal of Computer and Communications》 (电脑和通信（英文）)

年 卷 期：2015年第3卷第5期

页      面：107-117页

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Building a Productive Domain-Specific Cloud for Big Data Processing and Analytics Service 

摘      要：Cloud Computing as a disruptive technology, provides a dynamic, elastic and promising computing climate to tackle the challenges of big data processing and analytics. Hadoop and MapReduce are the widely used open source frameworks in Cloud Computing for storing and processing big data in the scalable fashion. Spark is the latest parallel computing engine working together with Hadoop that exceeds MapReduce performance via its in-memory computing and high level programming features. In this paper, we present our design and implementation of a productive, domain-specific big data analytics cloud platform on top of Hadoop and Spark. To increase user’s productivity, we created a variety of data processing templates to simplify the programming efforts. We have conducted experiments for its productivity and performance with a few basic but representative data processing algorithms in the petroleum industry. Geophysicists can use the platform to productively design and implement scalable seismic data processing algorithms without handling the details of data management and the complexity of parallelism. The Cloud platform generates a complete data processing application based on user’s kernel program and simple configurations, allocates resources and executes it in parallel on top of Spark and Hadoop.