Pub Date : 2009-12-09DOI: 10.1109/e-Science.2009.26
Andreas V. Hense, Florian Quadt, Matthias Römer
Mobile devices as the iPhone and Android-based smartphones can be turned into useful tools for researchers in the field. The concept of a scholarly or scientific workbench can be extended by a mobile usage scenario, where the researcher takes photographs, records sounds, and performs basic metadata editing. We present the prototype and the architecture of a mobile workbench that uploads geo-tagged photographs taken by the camera of the smartphone into a digital repository. We discuss the idea of a research workbench as an integrated but loosely-coupled set of services; we show where in our solution the functionalities of a research workbench are contained or where other services can be added. Finally, we focus on the software running on the mobile phone, which is in itself a set of loosely-coupled applications.
{"title":"Towards a Mobile Workbench for Researchers","authors":"Andreas V. Hense, Florian Quadt, Matthias Römer","doi":"10.1109/e-Science.2009.26","DOIUrl":"https://doi.org/10.1109/e-Science.2009.26","url":null,"abstract":"Mobile devices as the iPhone and Android-based smartphones can be turned into useful tools for researchers in the field. The concept of a scholarly or scientific workbench can be extended by a mobile usage scenario, where the researcher takes photographs, records sounds, and performs basic metadata editing. We present the prototype and the architecture of a mobile workbench that uploads geo-tagged photographs taken by the camera of the smartphone into a digital repository. We discuss the idea of a research workbench as an integrated but loosely-coupled set of services; we show where in our solution the functionalities of a research workbench are contained or where other services can be added. Finally, we focus on the software running on the mobile phone, which is in itself a set of loosely-coupled applications.","PeriodicalId":325840,"journal":{"name":"2009 Fifth IEEE International Conference on e-Science","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128975265","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-12-09DOI: 10.1109/E-SCIENCE.2009.44
Travis Desell, M. Magdon-Ismail, B. Szymanski, Carlos A. Varela, H. Newberg, N. Cole
Volunteer computing grids offer significant computing power at relatively low cost to researchers, while at the same time generating public interest in different scientific projects. However, in order to be used effectively, their heterogeneity, volatility and restrictive computing models must be overcome. As these computing grids are open, incorrect or malicious results must also be handled. This paper examines extending the BOINC volunteer computing framework to allow for asynchronous global optimization as applied to scientific computing problems. The asynchronous optimization method used is resilient to faults and the heterogeneous nature of volunteer computing grids, while allowing scalability to tens of thousands of hosts. A work verification strategy that does not require the validation of every result is presented. This is shown to be able to effectively reduce the need for verification done to less than 30% of the reported results, without degrading the performance of the asynchronous search methods. An asynchronous version of particle swarm optimization (APSO) is presented and com- pared to previously used asynchronous genetic search (AGS) using the MilkyWay@Home BOINC computing project. Both search methods are shown to scale to MilkyWay@Home’s current user base, over 75,000 heterogeneous and volatile hosts, something not possible for traditional optimization methods. APSO is shown to provide faster convergence to optimal results while being less sensitive to its search parameters. The verification strategy presented is shown to be effective for both AGS and APSO.
{"title":"Robust Asynchronous Optimization for Volunteer Computing Grids","authors":"Travis Desell, M. Magdon-Ismail, B. Szymanski, Carlos A. Varela, H. Newberg, N. Cole","doi":"10.1109/E-SCIENCE.2009.44","DOIUrl":"https://doi.org/10.1109/E-SCIENCE.2009.44","url":null,"abstract":"Volunteer computing grids offer significant computing power at relatively low cost to researchers, while at the same time generating public interest in different scientific projects. However, in order to be used effectively, their heterogeneity, volatility and restrictive computing models must be overcome. As these computing grids are open, incorrect or malicious results must also be handled. This paper examines extending the BOINC volunteer computing framework to allow for asynchronous global optimization as applied to scientific computing problems. The asynchronous optimization method used is resilient to faults and the heterogeneous nature of volunteer computing grids, while allowing scalability to tens of thousands of hosts. A work verification strategy that does not require the validation of every result is presented. This is shown to be able to effectively reduce the need for verification done to less than 30% of the reported results, without degrading the performance of the asynchronous search methods. An asynchronous version of particle swarm optimization (APSO) is presented and com- pared to previously used asynchronous genetic search (AGS) using the MilkyWay@Home BOINC computing project. Both search methods are shown to scale to MilkyWay@Home’s current user base, over 75,000 heterogeneous and volatile hosts, something not possible for traditional optimization methods. APSO is shown to provide faster convergence to optimal results while being less sensitive to its search parameters. The verification strategy presented is shown to be effective for both AGS and APSO.","PeriodicalId":325840,"journal":{"name":"2009 Fifth IEEE International Conference on e-Science","volume":"144 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132233464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-12-09DOI: 10.1109/e-Science.2009.30
M. Lassnig, T. Fahringer, V. Garonne, A. Molfetas, M. Branco
We present a probabilistic tracing method that captures both user and system behaviour for large-scale distributed applications. Our method extends the notion of data stream monitoring to work within what we define as concealed environments. We detail the conceptual design and implementation of our method. Additionally, we evaluate the scalability of the tracing method in a real petabyte-scale distributed data management system. Finally, we demonstrate the usefulness of the collected trace data in three scenarios. First, we use collected trace data to examine the arrival of user events and find self-similar processes. Second, we examine the behaviour and performance of mass storage systems in a grid under concurrent requests. Third, we develop a model for prediction of user event arrivals based on historical data. Our results suggest that a probabilistic tracing method is scalable, straightforward to integrate with existing applications, and provides useful insight into the behaviour of very large-scale applications.
{"title":"Stream Monitoring in Large-Scale Distributed Concealed Environments","authors":"M. Lassnig, T. Fahringer, V. Garonne, A. Molfetas, M. Branco","doi":"10.1109/e-Science.2009.30","DOIUrl":"https://doi.org/10.1109/e-Science.2009.30","url":null,"abstract":"We present a probabilistic tracing method that captures both user and system behaviour for large-scale distributed applications. Our method extends the notion of data stream monitoring to work within what we define as concealed environments. We detail the conceptual design and implementation of our method. Additionally, we evaluate the scalability of the tracing method in a real petabyte-scale distributed data management system. Finally, we demonstrate the usefulness of the collected trace data in three scenarios. First, we use collected trace data to examine the arrival of user events and find self-similar processes. Second, we examine the behaviour and performance of mass storage systems in a grid under concurrent requests. Third, we develop a model for prediction of user event arrivals based on historical data. Our results suggest that a probabilistic tracing method is scalable, straightforward to integrate with existing applications, and provides useful insight into the behaviour of very large-scale applications.","PeriodicalId":325840,"journal":{"name":"2009 Fifth IEEE International Conference on e-Science","volume":"28 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132468892","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-12-09DOI: 10.1109/e-Science.2009.29
C. Brooking, S. R. Shouldice, Gautier Robin, B. Kobe, Jennifer L. Martin, J. Hunter
This paper describes the set of eResearch services developed by the eResearch Lab within the University of Queensland (UQ) for the Structural Genomics (SG) Group at UQ. The aim of these services is to enable collaborative teams of protein crystallographers in the SG group to track their experiments and to manage the plethora and diversity of data that they generate through distributed high-throughput approaches and complex scientific workflows. More specifically we describe: the secure Web-based laboratory information management system (TIMTAM) and the X-ray diffraction image archive (DIMER) used to monitor experiments and record data captured prior to structure determination and the publication of a new crystal structure in public repositories such as the Protein Data bank (PDB). We also describe the services that we have developed to relate the different products generated at each stage in the protein crystallography pipeline through OAI-ORE compound objects. We conclude by comparing the OAI-ORE approach for publishing and sharing related scientific outcomes with the METS-based approach employed by other scientific laboratories.
{"title":"Comparing METS and OAI-ORE for Encapsulating Scientific Data Products: A Protein Crystallography Case Study","authors":"C. Brooking, S. R. Shouldice, Gautier Robin, B. Kobe, Jennifer L. Martin, J. Hunter","doi":"10.1109/e-Science.2009.29","DOIUrl":"https://doi.org/10.1109/e-Science.2009.29","url":null,"abstract":"This paper describes the set of eResearch services developed by the eResearch Lab within the University of Queensland (UQ) for the Structural Genomics (SG) Group at UQ. The aim of these services is to enable collaborative teams of protein crystallographers in the SG group to track their experiments and to manage the plethora and diversity of data that they generate through distributed high-throughput approaches and complex scientific workflows. More specifically we describe: the secure Web-based laboratory information management system (TIMTAM) and the X-ray diffraction image archive (DIMER) used to monitor experiments and record data captured prior to structure determination and the publication of a new crystal structure in public repositories such as the Protein Data bank (PDB). We also describe the services that we have developed to relate the different products generated at each stage in the protein crystallography pipeline through OAI-ORE compound objects. We conclude by comparing the OAI-ORE approach for publishing and sharing related scientific outcomes with the METS-based approach employed by other scientific laboratories.","PeriodicalId":325840,"journal":{"name":"2009 Fifth IEEE International Conference on e-Science","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116669430","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-12-09DOI: 10.1109/E-SCIENCE.2009.25
M. Humphrey, D. Agarwal, C. Ingen
Many of today’s large-scale scientific projects attempt to collect data from a diverse set of sources. The traditional campaign-style approach to “synthesis” efforts gathers data through a single concentrated effort, and the data contributors know in advance exactly who will use their data and why. At even moderate scales, the cost and time required to find, gather, collate, normalize, and customize data in order to build a synthesis dataset can quickly outweigh the value of the resulting dataset. By explicitly identifying and addressing the different requirements for each data role (author, publisher, curator, and consumer), our data management architecture for large-scale shared scientific data enables the creation of such synthesis datasets that continue to grow and evolve with new data, data annotations, participants, and use rules. We show the effectiveness of our approach in the context of the FLUXNET Synthesis Dataset, one of the largest ongoing biogeophysical experiments.
{"title":"Fluxdata.org: Publication and Curation of Shared Scientific Climate and Earth Sciences Data","authors":"M. Humphrey, D. Agarwal, C. Ingen","doi":"10.1109/E-SCIENCE.2009.25","DOIUrl":"https://doi.org/10.1109/E-SCIENCE.2009.25","url":null,"abstract":"Many of today’s large-scale scientific projects attempt to collect data from a diverse set of sources. The traditional campaign-style approach to “synthesis” efforts gathers data through a single concentrated effort, and the data contributors know in advance exactly who will use their data and why. At even moderate scales, the cost and time required to find, gather, collate, normalize, and customize data in order to build a synthesis dataset can quickly outweigh the value of the resulting dataset. By explicitly identifying and addressing the different requirements for each data role (author, publisher, curator, and consumer), our data management architecture for large-scale shared scientific data enables the creation of such synthesis datasets that continue to grow and evolve with new data, data annotations, participants, and use rules. We show the effectiveness of our approach in the context of the FLUXNET Synthesis Dataset, one of the largest ongoing biogeophysical experiments.","PeriodicalId":325840,"journal":{"name":"2009 Fifth IEEE International Conference on e-Science","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128467958","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-12-01DOI: 10.1109/E-SCIENCE.2009.31
A. Burton, A. Treloar
This paper first provides an overview of the e-Research environment in Australia, before introducing the two organizations (the Australian National Data Service – ANDS, and the Australian Research Collaboration Services - ARCS) whose services will be focus of the paper. Next the paper describes the problem that needs to be solved before talking about the requirements for management of data and metadata. The paper then discusses the main ARCS and ANDS services that will be combined to provide Publish My Data. The paper provides an overview of how this combined service will work for data that is already stored and data not yet stored, before concluding.
{"title":"Publish My Data: A Composition of Services from ANDS and ARCS","authors":"A. Burton, A. Treloar","doi":"10.1109/E-SCIENCE.2009.31","DOIUrl":"https://doi.org/10.1109/E-SCIENCE.2009.31","url":null,"abstract":"This paper first provides an overview of the e-Research environment in Australia, before introducing the two organizations (the Australian National Data Service – ANDS, and the Australian Research Collaboration Services - ARCS) whose services will be focus of the paper. Next the paper describes the problem that needs to be solved before talking about the requirements for management of data and metadata. The paper then discusses the main ARCS and ANDS services that will be combined to provide Publish My Data. The paper provides an overview of how this combined service will work for data that is already stored and data not yet stored, before concluding.","PeriodicalId":325840,"journal":{"name":"2009 Fifth IEEE International Conference on e-Science","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130522871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-12-01DOI: 10.1109/e-Science.2009.47
J. Koetsier, Andrew Turner, Patricia Richardson, Jano van Hemert
In this study, we apply a methodology for rapid development of portlets for scientific computing to the domain of computational chemistry. We report results in terms of the portals delivered, the changes made to our methodology and the experience gained in terms of interaction with domain-specialists. Our major contributions are: several web portals for teaching and research in computational chemistry; a successful transition to having our development tool used by the domain specialist as opposed by us, the developers; and an updated version of our methodology and technology for rapid development of portlets for computational science, which is free for anyone to pick up and use.
{"title":"Rapid Chemistry Portals through Engaging Researchers","authors":"J. Koetsier, Andrew Turner, Patricia Richardson, Jano van Hemert","doi":"10.1109/e-Science.2009.47","DOIUrl":"https://doi.org/10.1109/e-Science.2009.47","url":null,"abstract":"In this study, we apply a methodology for rapid development of portlets for scientific computing to the domain of computational chemistry. We report results in terms of the portals delivered, the changes made to our methodology and the experience gained in terms of interaction with domain-specialists. Our major contributions are: several web portals for teaching and research in computational chemistry; a successful transition to having our development tool used by the domain specialist as opposed by us, the developers; and an updated version of our methodology and technology for rapid development of portlets for computational science, which is free for anyone to pick up and use.","PeriodicalId":325840,"journal":{"name":"2009 Fifth IEEE International Conference on e-Science","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123546158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-12-01DOI: 10.1109/E-SCIENCE.2009.58
Hyunjoo Kim, Y. E. Khamra, S. Jha, M. Parashar
Clouds are rapidly joining high-performance Grids as viable computational platforms for scientific exploration and discovery, and it is clear that production computational infrastructures will integrate both these paradigms in the near future. As a result, understanding usage modes that are meaningful in such a hybrid infrastructure is critical. For example, there are interesting application workflows that can benefit from such hybrid usage modes to, per- haps, reduce times to solutions, reduce costs (in terms of currency or resource allocation), or handle unexpected runtime situations (e.g., unexpected delays in scheduling queues or unexpected failures). The primary goal of this paper is to experimentally investigate, from an applications perspective, how autonomics can enable interesting usage modes and scenarios for integrating HPC Grid and Clouds. Specifically, we used a reservoir characterization application workflow, based on Ensemble Kalman Filters (EnKF) for history matching, and the CometCloud autonomic Cloud engine on a hybrid platform consisting of the TeraGrid and Amazon EC2, to investigate 3 usage modes (or autonomic objectives) – acceleration, conservation and resilience.
{"title":"An Autonomic Approach to Integrated HPC Grid and Cloud Usage","authors":"Hyunjoo Kim, Y. E. Khamra, S. Jha, M. Parashar","doi":"10.1109/E-SCIENCE.2009.58","DOIUrl":"https://doi.org/10.1109/E-SCIENCE.2009.58","url":null,"abstract":"Clouds are rapidly joining high-performance Grids as viable computational platforms for scientific exploration and discovery, and it is clear that production computational infrastructures will integrate both these paradigms in the near future. As a result, understanding usage modes that are meaningful in such a hybrid infrastructure is critical. For example, there are interesting application workflows that can benefit from such hybrid usage modes to, per- haps, reduce times to solutions, reduce costs (in terms of currency or resource allocation), or handle unexpected runtime situations (e.g., unexpected delays in scheduling queues or unexpected failures). The primary goal of this paper is to experimentally investigate, from an applications perspective, how autonomics can enable interesting usage modes and scenarios for integrating HPC Grid and Clouds. Specifically, we used a reservoir characterization application workflow, based on Ensemble Kalman Filters (EnKF) for history matching, and the CometCloud autonomic Cloud engine on a hybrid platform consisting of the TeraGrid and Amazon EC2, to investigate 3 usage modes (or autonomic objectives) – acceleration, conservation and resilience.","PeriodicalId":325840,"journal":{"name":"2009 Fifth IEEE International Conference on e-Science","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126446167","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-12-01DOI: 10.1109/E-SCIENCE.2009.42
P. Bremer, G. Weber, Julien Tierny, Valerio Pascucci, M. Day, J. Bell
The advent of highly accurate, large scale volumetric simulations has made data analysis and visualization techniques an integral part of the modern scientific process. To develop new insights from raw data, scientists need the ability to define features of interest in a flexible manner and to understand how changes in the feature definition impact the subsequent analysis of the data. Therefore, simply exploring the raw data is not sufficient. This paper presents a new topological framework for the analysis of large scale, time-varying, turbulent combustion simulations. It allows the scientists to interactively explore the complete parameter space of fuel consumption thresholds for an entire time-dependent combustion simulation. By computing augmented merge trees and their corresponding data segmentations, the system allows the user complete flexibility to segment, select, and track burning cells through time thanks to a linked view interface. We developed this technique in the context of low-swirl turbulent pre-mixed same simulation analysis, where the topological abstractions enable an efficient tracking through time of the burning cells and provide new qualitative and quantitative insights into the dynamics of the combustion process.
{"title":"A Topological Framework for the Interactive Exploration of Large Scale Turbulent Combustion","authors":"P. Bremer, G. Weber, Julien Tierny, Valerio Pascucci, M. Day, J. Bell","doi":"10.1109/E-SCIENCE.2009.42","DOIUrl":"https://doi.org/10.1109/E-SCIENCE.2009.42","url":null,"abstract":"The advent of highly accurate, large scale volumetric simulations has made data analysis and visualization techniques an integral part of the modern scientific process. To develop new insights from raw data, scientists need the ability to define features of interest in a flexible manner and to understand how changes in the feature definition impact the subsequent analysis of the data. Therefore, simply exploring the raw data is not sufficient. This paper presents a new topological framework for the analysis of large scale, time-varying, turbulent combustion simulations. It allows the scientists to interactively explore the complete parameter space of fuel consumption thresholds for an entire time-dependent combustion simulation. By computing augmented merge trees and their corresponding data segmentations, the system allows the user complete flexibility to segment, select, and track burning cells through time thanks to a linked view interface. We developed this technique in the context of low-swirl turbulent pre-mixed same simulation analysis, where the topological abstractions enable an efficient tracking through time of the burning cells and provide new qualitative and quantitative insights into the dynamics of the combustion process.","PeriodicalId":325840,"journal":{"name":"2009 Fifth IEEE International Conference on e-Science","volume":"190 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121083813","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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