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Leveraging the Cloud for Robust and Efficient Lunar Image ProcessingThe Lunar Mapping and Modeling Project (LMMP) is tasked to aggregate lunar data, from the Apollo era to the latest instruments on the LRO spacecraft, into a central repository accessible by scientists and the general public. A critical function of this task is to provide users with the best solution for browsing the vast amounts of imagery available. The image files LMMP manages range from a few gigabytes to hundreds of gigabytes in size with new data arriving every day. Despite this ever-increasing amount of data, LMMP must make the data readily available in a timely manner for users to view and analyze. This is accomplished by tiling large images into smaller images using Hadoop, a distributed computing software platform implementation of the MapReduce framework, running on a small cluster of machines locally. Additionally, the software is implemented to use Amazon's Elastic Compute Cloud (EC2) facility. We also developed a hybrid solution to serve images to users by leveraging cloud storage using Amazon's Simple Storage Service (S3) for public data while keeping private information on our own data servers. By using Cloud Computing, we improve upon our local solution by reducing the need to manage our own hardware and computing infrastructure, thereby reducing costs. Further, by using a hybrid of local and cloud storage, we are able to provide data to our users more efficiently and securely. 12 This paper examines the use of a distributed approach with Hadoop to tile images, an approach that provides significant improvements in image processing time, from hours to minutes. This paper describes the constraints imposed on the solution and the resulting techniques developed for the hybrid solution of a customized Hadoop infrastructure over local and cloud resources in managing this ever-growing data set. It examines the performance trade-offs of using the more plentiful resources of the cloud, such as those provided by S3, against the bandwidth limitations such use encounters with remote resources. As part of this discussion this paper will outline some of the technologies employed, the reasons for their selection, the resulting performance metrics and the direction the project is headed based upon the demonstrated capabilities thus far.
Document ID
20150008828
Document Type
Conference Paper
External Source(s)
Authors
Chang, George (Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Malhotra, Shan (Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Wolgast, Paul (Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Date Acquired
May 26, 2015
Publication Date
March 5, 2011
Subject Category
Lunar and Planetary Science and Exploration
Meeting Information
2011 IEEE Aerospace Conference(Big Sky, MT)
Distribution Limits
Public
Copyright
Other
Keywords
Cloud Computing
Amazon EC2
LMMP
MapReduce
Hadoop