Home
Resource
Pilot based frameworks for weather research forecasting
PDF
PDF format is widely accepted and good for printing.
Plug-in required
PDF-1(1.07 MB)
Citation & Export
View Usage Statistics
Staff View

Citation & Export
Hide

Simple citation

Ganapathi, Dinesh Prasanth. Pilot based frameworks for weather research forecasting. Retrieved from https://doi.org/doi:10.7282/T3VQ34D8

Export

Click here for information about Citation Management Tools at Rutgers.

Statistics
Hide

Description

TitlePilot based frameworks for weather research forecasting
NameGanapathi, Dinesh Prasanth (author); Jha, Shantenu (chair); Spasojevic, Predrag (internal member); Zonouz, Saman Aliari (internal member); Rutgers University; Graduate School - New Brunswick
Date Created2015
Other Date2015-01 (degree)
SubjectElectrical and Computer Engineering, Weather forecasting, Cyberinfrastructure, Computer networks
Extent1 online resource (x, 40 p. : ill.)
DescriptionThe Weather Research Forecasting (WRF) domain consists of complex workflows that demand the use of Distributed Computing Infrastructure (DCI). Weather forecasting requires that weather researchers use different set of initial conditions and one or a combination of physics models on the same set of input data. For these type of simulations an ensemble based computing approach becomes imperative. Most DCIs have local job-schedulers that have no smart way of dealing with the execution of an ensemble type of computational problem as the job-schedulers are built to cater to the bare essentials of resource allocation. This means the weather scientists have to submit multiple jobs to the job-scheduler. In this dissertation we use Pilot-Job based tools to decouple work-load submission and resource allocation therefore streamlining the complex workflows in Weather Research and Forecasting domain and reduce their overall time to completion. We also achieve location independent job execution, data movement, placement and processing. Next, we create the necessary enablers to run an ensemble of tasks bearing the capability to run on multiple heterogeneous distributed computing resources there by creating the opportunity to minimize the overall time consumed in running the models. Our experiments show that the tools developed exhibit very good, strong and weak scaling characteristics. These results bear the potential to change the way weather researchers are submitting traditional WRF jobs to the DCIs by giving them a powerful weapon in their arsenal that can exploit the combined power of various heterogeneous DCIs that could otherwise be difficult to harness owing to interoperability issues.
NoteM.S.
NoteIncludes bibliographical references
Noteby Dinesh Prasanth Ganapathi
Genretheses, ETD graduate
Persistent URLhttps://doi.org/doi:10.7282/T3VQ34D8
Languageeng
CollectionGraduate School - New Brunswick Electronic Theses and Dissertations
Organization NameRutgers, The State University of New Jersey
RightsThe author owns the copyright to this work.
Version 8.5.5
Rutgers University Libraries - Copyright ©2024