GINJA: One-dollar Cloud-based Disaster Recovery for Databases
| Main Authors: | Alcântara, Joel, Oliveira, Tiago, Bessani; Alysson |
|---|---|
| Format: | Proceeding eJournal |
| Bahasa: | eng |
| Terbitan: |
, 2017
|
| Subjects: | |
| Online Access: |
https://zenodo.org/record/1163572 |
| ctrlnum |
1163572 |
|---|---|
| fullrecord |
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<dc schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"><creator>Alcântara, Joel</creator><creator>Oliveira, Tiago</creator><creator>Bessani; Alysson</creator><date>2017-12-15</date><description>Disaster Recovery (DR) is a crucial feature to ensure availability and data protection in modern information systems. A common DR
approach requires the replication of services in a set of virtual machines running in the cloud as backups. This leads to considerable
monetary costs and managing efforts to keep such cloud VMs. We present GINJA, a DR solution for transactional database management systems (DBMS) that uses only cloud storage services such as Amazon S3. GINJA works at file-system level to efficiently capture and replicate data updates to a remote cloud storage service, achieving three important goals: (1) reduces the costs for maintaining a cloud-based DR to less than one dollar per month for relevant databases’ sizes and workloads (up to 222× less than the traditional approach of having a DBMS replica in a cloud VM); (2) allows a precise control of the operational costs, durability and performance trade-offs; and (3) introduces a small performance overhead to the DBMS (e.g., less than 5% overhead for the TPC-C workload with ≈ 10 seconds of data loss in case of disasters).</description><identifier>https://zenodo.org/record/1163572</identifier><identifier>10.5281/zenodo.1163572</identifier><identifier>oai:zenodo.org:1163572</identifier><language>eng</language><relation>info:eu-repo/grantAgreement/EC/H2020/643964/</relation><relation>doi:10.5281/zenodo.1163571</relation><relation>url:https://zenodo.org/communities/supercloud</relation><rights>info:eu-repo/semantics/openAccess</rights><rights>https://creativecommons.org/licenses/by/4.0/legalcode</rights><subject>Disaster recovery</subject><subject>Databases</subject><subject>Cloud</subject><title>GINJA: One-dollar Cloud-based Disaster Recovery for Databases</title><type>Journal:Proceeding</type><type>Journal:Proceeding</type><recordID>1163572</recordID></dc>
|
| language |
eng |
| format |
Journal:Proceeding Journal Journal:eJournal |
| author |
Alcântara, Joel Oliveira, Tiago Bessani; Alysson |
| title |
GINJA: One-dollar Cloud-based Disaster Recovery for Databases |
| publishDate |
2017 |
| topic |
Disaster recovery Databases Cloud |
| url |
https://zenodo.org/record/1163572 |
| contents |
Disaster Recovery (DR) is a crucial feature to ensure availability and data protection in modern information systems. A common DR
approach requires the replication of services in a set of virtual machines running in the cloud as backups. This leads to considerable
monetary costs and managing efforts to keep such cloud VMs. We present GINJA, a DR solution for transactional database management systems (DBMS) that uses only cloud storage services such as Amazon S3. GINJA works at file-system level to efficiently capture and replicate data updates to a remote cloud storage service, achieving three important goals: (1) reduces the costs for maintaining a cloud-based DR to less than one dollar per month for relevant databases’ sizes and workloads (up to 222× less than the traditional approach of having a DBMS replica in a cloud VM); (2) allows a precise control of the operational costs, durability and performance trade-offs; and (3) introduces a small performance overhead to the DBMS (e.g., less than 5% overhead for the TPC-C workload with ≈ 10 seconds of data loss in case of disasters). |
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IOS17403.1163572 |
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Universitas PGRI Palembang |
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189 |
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Perpustakaan Universitas PGRI Palembang |
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587 |
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Marga Life in South Sumatra in the Past: Puyang Concept Sacrificed and Demythosized |
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17403 |
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KOTA PALEMBANG |
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SUMATERA SELATAN |
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IOS17403 |
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2022-07-26T02:01:05Z |
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2022-07-26T02:01:05Z |
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17.610468 |