Article citationsMore>>

Yu, Y., Isard, M., Fetterly, D., Budiu, M., Erlingsson, U., Gunda, P.K. and Currey, J. (2008) Dryadlinq: A System for General-Purpose Distributed Data Parallel Computing Using a High-Level Language. OSDI, 8, 1-4.

has been cited by the following article:

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

  AUTHORS: Yuzhong Yan, Mahsa Hanifi, Liqi Yi, Lei Huang

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

  JOURNAL NAME: Journal of Computer and Communications, Vol.3 No.5, May 25, 2015

  ABSTRACT: 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.