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The Experts below are selected from a list of 1464 Experts worldwide ranked by ideXlab platform

Kenneth Van Surksum - One of the best experts on this subject based on the ideXlab platform.

Po Chen - One of the best experts on this subject based on the ideXlab platform.

  • A cloud-based synthetic seismogram generator implemented using Windows Azure
    Earthquake Science, 2013
    Co-Authors: Po Chen, Liqiang Wang
    Abstract:

    Synthetic seismograms generated by solving the seismic wave equation using numerical methods are being widely used in seismology. For fully three-dimensional seismic structure models, the generation of these synthetic seismograms may require large amount of computing resources. Conventional high-performance computer clusters may not provide a cost-effective solution to this type of applications. The newly emerging cloud-computing platform provides an alternative solution. In this paper, we describe our implementation of a synthetic seismogram generator based on the reciprocity principle using the Windows Azure cloud application framework. Our preliminary experiment shows that our cloud-based synthetic seismogram generator provides a cost-effective and numerically efficient approach for computing synthetic seismograms based on the reciprocity principle.

  • Rapid 3D Seismic Source Inversion Using Windows Azure and Amazon EC2
    2011 IEEE World Congress on Services, 2011
    Co-Authors: Vedaprakash Subramanian, Liqiang Wang, Po Chen
    Abstract:

    With its rapid development, cloud computing has been increasingly adopted by scientists for large-scale scientific computation. Compared to the traditional computing platforms such as cluster and supercomputer, cloud computing is more elastic in the support of real-time computation and more powerful in the management of large-scale datasets. This paper presents our experience on designing and implementing seismic source inversion on both cluster (specifically, MPI-based) and cloud computing (specifically, Amazon EC2 and Microsoft Windows Azure). Our experiment shows that applying cloud computing to seismic source inversion is feasible and has its advantages. In addition, we notice that both cluster and Amazon EC2 have obviously better performance than Windows Azure. Cloud computing is suited for real-time processing scientific applications but it (especially Azure) does not work well for tightly-coupled applications.

  • SERVICES - Rapid 3D Seismic Source Inversion Using Windows Azure and Amazon EC2
    2011 IEEE World Congress on Services, 2011
    Co-Authors: Vedaprakash Subramanian, Liqiang Wang, Hongyi Ma, Po Chen
    Abstract:

    With its rapid development, cloud computing has been increasingly adopted by scientists for large-scale scientific computation. Compared to the traditional computing platforms such as cluster and supercomputer, cloud computing is more elastic in the support of real-time computation and more powerful in the management of large-scale datasets. This paper presents our experience on designing and implementing seismic source inversion on both cluster (specifically, MPI-based) and cloud computing (specifically, Amazon EC2 and Microsoft Windows Azure). Our experiment shows that applying cloud computing to seismic source inversion is feasible and has its advantages. In addition, we notice that both cluster and Amazon EC2 have obviously better performance than Windows Azure. Cloud computing is suited for real-time processing scientific applications but it (especially Azure) does not work well for tightly-coupled applications.

  • CloudCom - Rapid Processing of Synthetic Seismograms Using Windows Azure Cloud
    2010 IEEE Second International Conference on Cloud Computing Technology and Science, 2010
    Co-Authors: Vedaprakash Subramanian, Liqiang Wang, Po Chen
    Abstract:

    Currently, numerically simulated synthetic seismograms are widely used by seismologists for seismological inferences. The generation of these synthetic seismograms requires large amount of computing resources, and the maintenance of these observed seismograms requires massive storage. Traditional high-performance computing platforms is inefficient to handle these applications because rapid computations are needed and large-scale datasets should be maintained. The emerging cloud computing platform provides an efficient substitute. In this paper, we introduce our experience on implementing a computational platform for rapidly computing and delivering synthetic seismograms on Windows Azure. Our experiment shows that cloud computing is an ideal platform for such kind of applications.

  • Rapid Processing of Synthetic Seismograms Using Windows Azure Cloud
    2010 IEEE Second International Conference on Cloud Computing Technology and Science, 2010
    Co-Authors: Vedaprakash Subramanian, Liqiang Wang, Po Chen
    Abstract:

    Currently, numerically simulated synthetic seismograms are widely used by seismologists for seismological inferences. The generation of these synthetic seismograms requires large amount of computing resources, and the maintenance of these observed seismograms requires massive storage. Traditional high-performance computing platforms is inefficient to handle these applications because rapid computations are needed and large-scale datasets should be maintained. The emerging cloud computing platform provides an efficient substitute. In this paper, we introduce our experience on implementing a computational platform for rapidly computing and delivering synthetic seismograms on Windows Azure. Our experiment shows that cloud computing is an ideal platform for such kind of applications.

Vedaprakash Subramanian - One of the best experts on this subject based on the ideXlab platform.

  • Rapid 3D Seismic Source Inversion Using Windows Azure and Amazon EC2
    2011 IEEE World Congress on Services, 2011
    Co-Authors: Vedaprakash Subramanian, Liqiang Wang, Po Chen
    Abstract:

    With its rapid development, cloud computing has been increasingly adopted by scientists for large-scale scientific computation. Compared to the traditional computing platforms such as cluster and supercomputer, cloud computing is more elastic in the support of real-time computation and more powerful in the management of large-scale datasets. This paper presents our experience on designing and implementing seismic source inversion on both cluster (specifically, MPI-based) and cloud computing (specifically, Amazon EC2 and Microsoft Windows Azure). Our experiment shows that applying cloud computing to seismic source inversion is feasible and has its advantages. In addition, we notice that both cluster and Amazon EC2 have obviously better performance than Windows Azure. Cloud computing is suited for real-time processing scientific applications but it (especially Azure) does not work well for tightly-coupled applications.

  • SERVICES - Rapid 3D Seismic Source Inversion Using Windows Azure and Amazon EC2
    2011 IEEE World Congress on Services, 2011
    Co-Authors: Vedaprakash Subramanian, Liqiang Wang, Hongyi Ma, Po Chen
    Abstract:

    With its rapid development, cloud computing has been increasingly adopted by scientists for large-scale scientific computation. Compared to the traditional computing platforms such as cluster and supercomputer, cloud computing is more elastic in the support of real-time computation and more powerful in the management of large-scale datasets. This paper presents our experience on designing and implementing seismic source inversion on both cluster (specifically, MPI-based) and cloud computing (specifically, Amazon EC2 and Microsoft Windows Azure). Our experiment shows that applying cloud computing to seismic source inversion is feasible and has its advantages. In addition, we notice that both cluster and Amazon EC2 have obviously better performance than Windows Azure. Cloud computing is suited for real-time processing scientific applications but it (especially Azure) does not work well for tightly-coupled applications.

  • CloudCom - Rapid Processing of Synthetic Seismograms Using Windows Azure Cloud
    2010 IEEE Second International Conference on Cloud Computing Technology and Science, 2010
    Co-Authors: Vedaprakash Subramanian, Liqiang Wang, Po Chen
    Abstract:

    Currently, numerically simulated synthetic seismograms are widely used by seismologists for seismological inferences. The generation of these synthetic seismograms requires large amount of computing resources, and the maintenance of these observed seismograms requires massive storage. Traditional high-performance computing platforms is inefficient to handle these applications because rapid computations are needed and large-scale datasets should be maintained. The emerging cloud computing platform provides an efficient substitute. In this paper, we introduce our experience on implementing a computational platform for rapidly computing and delivering synthetic seismograms on Windows Azure. Our experiment shows that cloud computing is an ideal platform for such kind of applications.

  • Rapid Processing of Synthetic Seismograms Using Windows Azure Cloud
    2010 IEEE Second International Conference on Cloud Computing Technology and Science, 2010
    Co-Authors: Vedaprakash Subramanian, Liqiang Wang, Po Chen
    Abstract:

    Currently, numerically simulated synthetic seismograms are widely used by seismologists for seismological inferences. The generation of these synthetic seismograms requires large amount of computing resources, and the maintenance of these observed seismograms requires massive storage. Traditional high-performance computing platforms is inefficient to handle these applications because rapid computations are needed and large-scale datasets should be maintained. The emerging cloud computing platform provides an efficient substitute. In this paper, we introduce our experience on implementing a computational platform for rapidly computing and delivering synthetic seismograms on Windows Azure. Our experiment shows that cloud computing is an ideal platform for such kind of applications.

Liqiang Wang - One of the best experts on this subject based on the ideXlab platform.

  • A cloud-based synthetic seismogram generator implemented using Windows Azure
    Earthquake Science, 2013
    Co-Authors: Po Chen, Liqiang Wang
    Abstract:

    Synthetic seismograms generated by solving the seismic wave equation using numerical methods are being widely used in seismology. For fully three-dimensional seismic structure models, the generation of these synthetic seismograms may require large amount of computing resources. Conventional high-performance computer clusters may not provide a cost-effective solution to this type of applications. The newly emerging cloud-computing platform provides an alternative solution. In this paper, we describe our implementation of a synthetic seismogram generator based on the reciprocity principle using the Windows Azure cloud application framework. Our preliminary experiment shows that our cloud-based synthetic seismogram generator provides a cost-effective and numerically efficient approach for computing synthetic seismograms based on the reciprocity principle.

  • Rapid 3D Seismic Source Inversion Using Windows Azure and Amazon EC2
    2011 IEEE World Congress on Services, 2011
    Co-Authors: Vedaprakash Subramanian, Liqiang Wang, Po Chen
    Abstract:

    With its rapid development, cloud computing has been increasingly adopted by scientists for large-scale scientific computation. Compared to the traditional computing platforms such as cluster and supercomputer, cloud computing is more elastic in the support of real-time computation and more powerful in the management of large-scale datasets. This paper presents our experience on designing and implementing seismic source inversion on both cluster (specifically, MPI-based) and cloud computing (specifically, Amazon EC2 and Microsoft Windows Azure). Our experiment shows that applying cloud computing to seismic source inversion is feasible and has its advantages. In addition, we notice that both cluster and Amazon EC2 have obviously better performance than Windows Azure. Cloud computing is suited for real-time processing scientific applications but it (especially Azure) does not work well for tightly-coupled applications.

  • SERVICES - Rapid 3D Seismic Source Inversion Using Windows Azure and Amazon EC2
    2011 IEEE World Congress on Services, 2011
    Co-Authors: Vedaprakash Subramanian, Liqiang Wang, Hongyi Ma, Po Chen
    Abstract:

    With its rapid development, cloud computing has been increasingly adopted by scientists for large-scale scientific computation. Compared to the traditional computing platforms such as cluster and supercomputer, cloud computing is more elastic in the support of real-time computation and more powerful in the management of large-scale datasets. This paper presents our experience on designing and implementing seismic source inversion on both cluster (specifically, MPI-based) and cloud computing (specifically, Amazon EC2 and Microsoft Windows Azure). Our experiment shows that applying cloud computing to seismic source inversion is feasible and has its advantages. In addition, we notice that both cluster and Amazon EC2 have obviously better performance than Windows Azure. Cloud computing is suited for real-time processing scientific applications but it (especially Azure) does not work well for tightly-coupled applications.

  • CloudCom - Rapid Processing of Synthetic Seismograms Using Windows Azure Cloud
    2010 IEEE Second International Conference on Cloud Computing Technology and Science, 2010
    Co-Authors: Vedaprakash Subramanian, Liqiang Wang, Po Chen
    Abstract:

    Currently, numerically simulated synthetic seismograms are widely used by seismologists for seismological inferences. The generation of these synthetic seismograms requires large amount of computing resources, and the maintenance of these observed seismograms requires massive storage. Traditional high-performance computing platforms is inefficient to handle these applications because rapid computations are needed and large-scale datasets should be maintained. The emerging cloud computing platform provides an efficient substitute. In this paper, we introduce our experience on implementing a computational platform for rapidly computing and delivering synthetic seismograms on Windows Azure. Our experiment shows that cloud computing is an ideal platform for such kind of applications.

  • Rapid Processing of Synthetic Seismograms Using Windows Azure Cloud
    2010 IEEE Second International Conference on Cloud Computing Technology and Science, 2010
    Co-Authors: Vedaprakash Subramanian, Liqiang Wang, Po Chen
    Abstract:

    Currently, numerically simulated synthetic seismograms are widely used by seismologists for seismological inferences. The generation of these synthetic seismograms requires large amount of computing resources, and the maintenance of these observed seismograms requires massive storage. Traditional high-performance computing platforms is inefficient to handle these applications because rapid computations are needed and large-scale datasets should be maintained. The emerging cloud computing platform provides an efficient substitute. In this paper, we introduce our experience on implementing a computational platform for rapidly computing and delivering synthetic seismograms on Windows Azure. Our experiment shows that cloud computing is an ideal platform for such kind of applications.

Tejaswi Redkar - One of the best experts on this subject based on the ideXlab platform.

  • VM Role and Windows Azure Connect
    Windows Azure Platform, 2020
    Co-Authors: Tejaswi Redkar, Tony Guidici
    Abstract:

    As two of the latest additions to the Windows Azure platform, it’s hard to think of any more widely anticipated features than VM role and Windows Azure Connect. However, they may also be the most misunderstood. In this chapter, I will help you understand why these features were released, what their purpose is, and the scenarios to which they do and do not apply.

  • Windows Azure Storage Part I — Blobs and Drives
    Windows Azure Platform, 2020
    Co-Authors: Tejaswi Redkar, Tony Guidici
    Abstract:

    The previous chapter covered computational and management features of Windows Azure. In this chapter, you will learn about Windows Azure Storage service. Windows Azure Storage is a scalable, highly available, and durable service for storing any kind of application and non-application data. The Storage service provides you with the ability to store data in three different types of storage types: blobs, queues, and tables. Each storage type has advantages; depending on the application requirements, you can choose the appropriate storage type for your data. You can also use multiple storage types within the same application.

  • Windows Azure Storage Part I — Blobs
    Windows Azure Platform, 2020
    Co-Authors: Tejaswi Redkar
    Abstract:

    The previous chapter covered the computational and management features of Windows Azure. In this chapter, you learn about Windows Azure’s Storage service feature. Windows Azure Storage is a scalable, highly available, and durable service to store any kind of application data. The Storage service provides you with the ability to store data in three different types of storage types: blobs, queues, and tables. Each storage type has advantages; depending on the application requirements, you can choose the appropriate storage type for your data. You can also use multiple storage types within the same application.

  • Windows Azure Storage Part III — Tables
    Windows Azure Platform, 2020
    Co-Authors: Tejaswi Redkar
    Abstract:

    The Windows Azure Table service provides structured storage in the cloud. Windows Azure tables aren’t relational database tables, but follow a simple yet highly flexible model of entities and properties. In the simplest of terms, tables contain entities, and entities have properties. The Table service is designed for massive scalability and availability, supporting billions of entities and terabytes of data. It’s designed to support high volume, but smaller sized objects. For example, you can use the Table service to store user profiles and session information in high-volume Internet sites. But if you also want to store the photos of users, you should store the images in the Blob storage and save the link to the photo in Table service, but Table service has limitations on the size of each entity object. In this chapter, you will learn about the Windows Azure Table Storage service in detail. I have covered all the basics you need to start developing with the Table Storage API and, like other chapters, I have dedicated a section for Table Storage scenarios.

  • Windows Azure Storage Part II — Queues
    2020
    Co-Authors: Tejaswi Redkar, Tony Guidici
    Abstract:

    The Windows Azure Queue service is an Internet-scale message queuing system for cross-service communications. Even though the service is called a queue, the messages aren’t guaranteed to follow the First In First Out (FIFO) pattern. The design focus of the Queue service is on providing a highly scalable and available asynchronous message communication system that’s accessible anywhere, anytime. The Queue service is not a replacement for your on-premises Microsoft Message Queuing (MSMQ), because it lacks some of the features like transactional messaging, distributed transactions, and integration with domain security. But, most of the applications that do not use these MSMQ features should be able to replace it by the Queue service with minor modifications. The Queue service provides a REST API for applications to use the large-scale Queue service infrastructure. If you want to build an application that is agnostic to the type of queuing system it uses, you should build an abstraction layer using interface contracts and then let the implementation decide the type of queue. In real-world programming, you should always start with interfaces whether you have multiple implementations or not.

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