The Experts below are selected from a list of 42 Experts worldwide ranked by ideXlab platform

Akshat Verma - One of the best experts on this subject based on the ideXlab platform.

  • 12MAP: Cloud Disaster Recovery Based on Image-Instance Mapping
    2013
    Co-Authors: Shripad Nadgowda, Praveen Jayachandran, Akshat Verma
    Abstract:

    Virtual machines (VMs) in a cloud use standardized ‘golden master’ images, standard software catalog and management tools. This facilitates quick provisioning of VMs and helps reduce the cost of managing the cloud by reducing the need for specialized software skills. However, knowledge of this similarity is lost post-provisioning, as VMs could experience different changes and may drift away from one another. In this work, we propose the 12MAP system, which maintains a mapping between each instance and the golden master image from which it was created, consisting of a record of all changes to the instance since provisioning. We motivate that this mapping can aid several cloud management activities such as disaster recovery, system administration, and troubleshooting. We build a host-based disaster recovery solution based on 12MAP, which is ideally suited for low cost cloud VMs that do not have access to dedicated block-based storage recovery solutions. Our solution deduplicates changes across VMs and needs to replicate only the unique changes, significantly reducing Replication Traffic on end hosts. We demonstrate that 12MAP is able to deliver on tight recovery time and recovery point objectives of the order of minutes with low overhead. Compared to state-of-the-art host-based recovery solutions, 12MAP is able to save 50-87% network bandwidth on the primary data center.

  • Middleware - 12MAP: Cloud Disaster Recovery Based on Image-Instance Mapping
    Middleware 2013, 2013
    Co-Authors: Shripad Nadgowda, Praveen Jayachandran, Akshat Verma
    Abstract:

    Virtual machines (VMs) in a cloud use standardized ‘golden master’ images, standard software catalog and management tools. This facilitates quick provisioning of VMs and helps reduce the cost of managing the cloud by reducing the need for specialized software skills. However, knowledge of this similarity is lost post-provisioning, as VMs could experience different changes and may drift away from one another. In this work, we propose the 12MAP system, which maintains a mapping between each instance and the golden master image from which it was created, consisting of a record of all changes to the instance since provisioning. We motivate that this mapping can aid several cloud management activities such as disaster recovery, system administration, and troubleshooting. We build a host-based disaster recovery solution based on 12MAP, which is ideally suited for low cost cloud VMs that do not have access to dedicated block-based storage recovery solutions. Our solution deduplicates changes across VMs and needs to replicate only the unique changes, significantly reducing Replication Traffic on end hosts. We demonstrate that 12MAP is able to deliver on tight recovery time and recovery point objectives of the order of minutes with low overhead. Compared to state-of-the-art host-based recovery solutions, 12MAP is able to save 50-87% network bandwidth on the primary data center.

Gregory R. Ganger - One of the best experts on this subject based on the ideXlab platform.

  • SIGMOD Conference - Online Deduplication for Databases
    Proceedings of the 2017 ACM International Conference on Management of Data - SIGMOD '17, 2017
    Co-Authors: Lianghong Xu, Andrew Pavlo, Sudipta Sengupta, Gregory R. Ganger
    Abstract:

    dbDedup is a similarity-based deduplication scheme for on-line database management systems (DBMSs). Beyond block-level compression of individual database pages or operation log (oplog) messages, as used in today's DBMSs, dbDedup uses byte-level delta encoding of individual records within the database to achieve greater savings. dbDedup's single-pass encoding method can be integrated into the storage and logging components of a DBMS to provide two benefits: (1) reduced size of data stored on disk beyond what traditional compression schemes provide, and (2) reduced amount of data transmitted over the network for Replication services. To evaluate our work, we implemented dbDedup in a distributed NoSQL DBMS and analyzed its properties using four real datasets. Our results show that dbDedup achieves up to 37x reduction in the storage size and Replication Traffic of the database on its own and up to 61x reduction when paired with the DBMS's block-level compression. dbDedup provides both benefits with negligible effect on DBMS throughput or client latency (average and tail).

  • Similarity-based Deduplication for Databases
    2017
    Co-Authors: Andrew Paylo, Sudipta Sengupta, Gregory R. Ganger
    Abstract:

    dDedup is a similarity-based deduplication scheme for on-line database management systems (DBMSs). Beyond block-level compression of individual database pages or operation log (oplog) messages, as used in today’s DBMSs, dDedup uses byte-level delta encoding of individual records within the database to achieve greater savings. dDedup’s single-pass encoding method can be integrated into the storage and logging components of a DBMS to provide two benefits: (1) reduced size of data stored on disk beyond what traditional compression schemes provide, and (2) reduced amount of data transmitted over the network for Replication services. To evaluate our work, we implemented dDedup in a distributed NoSQL DBMS and analyzed its properties using four real datasets. Our results show that dDedup achieves up to 37X reduction in the storage size and Replication Traffic of the database on its own and up to 61X reduction when paired with the DBMS’s block-level compression. dDedup provides both benefits with negligible effect on DBMS throughput or client latency (average and tail).

Shripad Nadgowda - One of the best experts on this subject based on the ideXlab platform.

  • 12MAP: Cloud Disaster Recovery Based on Image-Instance Mapping
    2013
    Co-Authors: Shripad Nadgowda, Praveen Jayachandran, Akshat Verma
    Abstract:

    Virtual machines (VMs) in a cloud use standardized ‘golden master’ images, standard software catalog and management tools. This facilitates quick provisioning of VMs and helps reduce the cost of managing the cloud by reducing the need for specialized software skills. However, knowledge of this similarity is lost post-provisioning, as VMs could experience different changes and may drift away from one another. In this work, we propose the 12MAP system, which maintains a mapping between each instance and the golden master image from which it was created, consisting of a record of all changes to the instance since provisioning. We motivate that this mapping can aid several cloud management activities such as disaster recovery, system administration, and troubleshooting. We build a host-based disaster recovery solution based on 12MAP, which is ideally suited for low cost cloud VMs that do not have access to dedicated block-based storage recovery solutions. Our solution deduplicates changes across VMs and needs to replicate only the unique changes, significantly reducing Replication Traffic on end hosts. We demonstrate that 12MAP is able to deliver on tight recovery time and recovery point objectives of the order of minutes with low overhead. Compared to state-of-the-art host-based recovery solutions, 12MAP is able to save 50-87% network bandwidth on the primary data center.

  • Middleware - 12MAP: Cloud Disaster Recovery Based on Image-Instance Mapping
    Middleware 2013, 2013
    Co-Authors: Shripad Nadgowda, Praveen Jayachandran, Akshat Verma
    Abstract:

    Virtual machines (VMs) in a cloud use standardized ‘golden master’ images, standard software catalog and management tools. This facilitates quick provisioning of VMs and helps reduce the cost of managing the cloud by reducing the need for specialized software skills. However, knowledge of this similarity is lost post-provisioning, as VMs could experience different changes and may drift away from one another. In this work, we propose the 12MAP system, which maintains a mapping between each instance and the golden master image from which it was created, consisting of a record of all changes to the instance since provisioning. We motivate that this mapping can aid several cloud management activities such as disaster recovery, system administration, and troubleshooting. We build a host-based disaster recovery solution based on 12MAP, which is ideally suited for low cost cloud VMs that do not have access to dedicated block-based storage recovery solutions. Our solution deduplicates changes across VMs and needs to replicate only the unique changes, significantly reducing Replication Traffic on end hosts. We demonstrate that 12MAP is able to deliver on tight recovery time and recovery point objectives of the order of minutes with low overhead. Compared to state-of-the-art host-based recovery solutions, 12MAP is able to save 50-87% network bandwidth on the primary data center.

Lianghong Xu - One of the best experts on this subject based on the ideXlab platform.

  • SIGMOD Conference - Online Deduplication for Databases
    Proceedings of the 2017 ACM International Conference on Management of Data - SIGMOD '17, 2017
    Co-Authors: Lianghong Xu, Andrew Pavlo, Sudipta Sengupta, Gregory R. Ganger
    Abstract:

    dbDedup is a similarity-based deduplication scheme for on-line database management systems (DBMSs). Beyond block-level compression of individual database pages or operation log (oplog) messages, as used in today's DBMSs, dbDedup uses byte-level delta encoding of individual records within the database to achieve greater savings. dbDedup's single-pass encoding method can be integrated into the storage and logging components of a DBMS to provide two benefits: (1) reduced size of data stored on disk beyond what traditional compression schemes provide, and (2) reduced amount of data transmitted over the network for Replication services. To evaluate our work, we implemented dbDedup in a distributed NoSQL DBMS and analyzed its properties using four real datasets. Our results show that dbDedup achieves up to 37x reduction in the storage size and Replication Traffic of the database on its own and up to 61x reduction when paired with the DBMS's block-level compression. dbDedup provides both benefits with negligible effect on DBMS throughput or client latency (average and tail).

Pascal Thubert - One of the best experts on this subject based on the ideXlab platform.