Service-oriented Scalable MODIS Aggregation Using STRATUS Framework
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Date
2020-01-01
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Department
Information Systems
Program
Information Systems
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This item may be protected under Title 17 of the U.S. Copyright Law. It is made available by UMBC for non-commercial research and education. For permission to publish or reproduce, please see http://aok.lib.umbc.edu/specoll/repro.php or contact Special Collections at speccoll(at)umbc.edu
This item may be protected under Title 17 of the U.S. Copyright Law. It is made available by UMBC for non-commercial research and education. For permission to publish or reproduce, please see http://aok.lib.umbc.edu/specoll/repro.php or contact Special Collections at speccoll(at)umbc.edu
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Abstract
The Moderate Resolution Imaging Spectroradiometer(MODIS) is a payload imaging sensor, a key instrument aboard NASA's Terra and Aqua satellites. The instrument is designed to capture high quality data in multiple spectral bands and at varying resolutions of the entire earth every 1 to 2 days for monitoring climate change. This data is typically in the range of terabytes, making it impractical for investigators and scientists to download such datasets for further analysis. This issue is addressed through Climate Analytics as a Service (CAaaS) approach which uses server-side computing. In CAaaS, the analytical operation resources are co-located with the data and the Analytic services enable users to perform operations on their desktop computers which are then executed remotely. Climate analytics services get more complex with diverse technologies and orchestration strategies. Hence, STRATUS framework is developed by NASA's Center for Climate Simulation. STRATUS is an integrated and flexible framework which helps in implementing CAaaS in a scalable and modular manner. In this work, we aim to provide an efficient implementation of the MODIS Aggregation function, an analytics operation used to compute statistics on cloud data, in a service oriented and distributed environment by integrating it into the STRATUS framework. While the service-oriented architecture provides the ability to run the MODIS Aggregation as an analytic service, the distributed setup allows the user to scale the aggregation function using a high-performance compute cluster, thus resulting in significant improvements in the inference times. In order to enable the STRATUS framework for the MODIS aggregation function, we implemented a new endpoint API and explored avenues to perform secure communication between the client and server via ssh-tunneling. Further, we also attempt to automate the entire process by providing scripts that enable automatic initialization of the server process and download of the required input files, thereby improving the overall efficiency of analysis procedure.