12MAP: Cloud Disaster Recovery Based on Image-Instance Mapping - Inria - Institut national de recherche en sciences et technologies du numérique
Communication Dans Un Congrès Année : 2013

12MAP: Cloud Disaster Recovery Based on Image-Instance Mapping

Shripad Nadgowda
  • Fonction : Auteur
  • PersonId : 1003223
Praveen Jayachandran
  • Fonction : Auteur
  • PersonId : 1003224
Akshat Verma
  • Fonction : Auteur
  • PersonId : 1003225

Résumé

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.
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hal-01480777 , version 1 (01-03-2017)

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Shripad Nadgowda, Praveen Jayachandran, Akshat Verma. 12MAP: Cloud Disaster Recovery Based on Image-Instance Mapping. 14th International Middleware Conference (Middleware), Dec 2013, Beijing, China. pp.204-225, ⟨10.1007/978-3-642-45065-5_11⟩. ⟨hal-01480777⟩
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