The fast growing market of mobile device adoption and cloud computing has led to exploitation of mobile devices utilizing cloud services. One major chal-lenge facing the usage of mobile devices in the cloud environmen...The fast growing market of mobile device adoption and cloud computing has led to exploitation of mobile devices utilizing cloud services. One major chal-lenge facing the usage of mobile devices in the cloud environment is mobile synchronization to the cloud, e.g., synchronizing contacts, text messages, imag-es, and videos. Owing to the expected high volume of traffic and high time complexity required for synchronization, an appropriate synchronization algo-rithm needs to be developed. Delta synchronization is one method of synchro-nizing compressed files that requires uploading the whole file, even when no changes were made or if it was only partially changed. In the present study, we proposed an algorithm, based on Delta synchronization, to solve the problem of synchronizing compressed files under various forms of modification (e.g., not modified, partially modified, or completely modified). To measure the effi-ciency of our proposed algorithm, we compared it to the Dropbox application algorithm. The results demonstrated that our algorithm outperformed the regular Dropbox synchronization mechanism by reducing the synchronization time, cost, and traffic load between clients and the cloud service provider.展开更多
One of the major scientific challenges and societal concerns is to make informed decisions to ensure sustainable groundwater availability when facing deep uncertainties.A major computational requirement associated wit...One of the major scientific challenges and societal concerns is to make informed decisions to ensure sustainable groundwater availability when facing deep uncertainties.A major computational requirement associated with this is on-demand computing for risk analysis to support timely decision.This paper presents a scientific modeling service called‘ModflowOnAzure’which enables large-scale ensemble runs of groundwater flow models to be easily executed in parallel in the Windows Azure cloud.Several technical issues were addressed,including the conjunctive use of desktop tools in MATLAB to avoid license issues in the cloud,integration of Dropbox with Azure for improved usability and‘Drop-and-Compute,’and automated file exchanges between desktop and the cloud.Two scientific use cases are presented in this paper using this service with significant computational speedup.One case is from Arizona,where six plausible alternative conceptual models and a streamflow stochastic model are used to evaluate the impacts of different groundwater pumping scenarios.Another case is from Texas,where a global sensitivity analysis is performed on a regional groundwater availability model.Results of both cases show informed uncertainty analysis results that can be used to assist the groundwater planning and sustainability study.展开更多
文摘The fast growing market of mobile device adoption and cloud computing has led to exploitation of mobile devices utilizing cloud services. One major chal-lenge facing the usage of mobile devices in the cloud environment is mobile synchronization to the cloud, e.g., synchronizing contacts, text messages, imag-es, and videos. Owing to the expected high volume of traffic and high time complexity required for synchronization, an appropriate synchronization algo-rithm needs to be developed. Delta synchronization is one method of synchro-nizing compressed files that requires uploading the whole file, even when no changes were made or if it was only partially changed. In the present study, we proposed an algorithm, based on Delta synchronization, to solve the problem of synchronizing compressed files under various forms of modification (e.g., not modified, partially modified, or completely modified). To measure the effi-ciency of our proposed algorithm, we compared it to the Dropbox application algorithm. The results demonstrated that our algorithm outperformed the regular Dropbox synchronization mechanism by reducing the synchronization time, cost, and traffic load between clients and the cloud service provider.
文摘One of the major scientific challenges and societal concerns is to make informed decisions to ensure sustainable groundwater availability when facing deep uncertainties.A major computational requirement associated with this is on-demand computing for risk analysis to support timely decision.This paper presents a scientific modeling service called‘ModflowOnAzure’which enables large-scale ensemble runs of groundwater flow models to be easily executed in parallel in the Windows Azure cloud.Several technical issues were addressed,including the conjunctive use of desktop tools in MATLAB to avoid license issues in the cloud,integration of Dropbox with Azure for improved usability and‘Drop-and-Compute,’and automated file exchanges between desktop and the cloud.Two scientific use cases are presented in this paper using this service with significant computational speedup.One case is from Arizona,where six plausible alternative conceptual models and a streamflow stochastic model are used to evaluate the impacts of different groundwater pumping scenarios.Another case is from Texas,where a global sensitivity analysis is performed on a regional groundwater availability model.Results of both cases show informed uncertainty analysis results that can be used to assist the groundwater planning and sustainability study.
