{"title":"An Integrated Cloud Platform for Rapid Interface Generation, Job Scheduling, Monitoring, Plotting, and Case Management of Scientific Applications","authors":"W. Brewer, W. Scott, John Sanford","doi":"10.1109/ICCCRI.2015.24","DOIUrl":null,"url":null,"abstract":"The Scientific Platform for the Cloud (SPC) presents a framework to support the rapid design and deployment of scientific applications (apps) in the cloud. It provides common infrastructure for running typical IXP (Input-execute-Plot) style apps, including: a web interface, post-processing and plotting capabilities, job scheduling, real-time monitoring of running jobs, and case manager. In this paper we (1) describe the design of the system architecture, (2) evaluate its applicability to a scientific workload, and (3) present a number of case studies which represent a wide variety of scientific applications including Population Genetics, Geophysics, Turbulence Physics, DNA analysis, and Big Data.","PeriodicalId":183970,"journal":{"name":"2015 International Conference on Cloud Computing Research and Innovation (ICCCRI)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 International Conference on Cloud Computing Research and Innovation (ICCCRI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCCRI.2015.24","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
The Scientific Platform for the Cloud (SPC) presents a framework to support the rapid design and deployment of scientific applications (apps) in the cloud. It provides common infrastructure for running typical IXP (Input-execute-Plot) style apps, including: a web interface, post-processing and plotting capabilities, job scheduling, real-time monitoring of running jobs, and case manager. In this paper we (1) describe the design of the system architecture, (2) evaluate its applicability to a scientific workload, and (3) present a number of case studies which represent a wide variety of scientific applications including Population Genetics, Geophysics, Turbulence Physics, DNA analysis, and Big Data.
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