research-article

Ginja: one-dollar cloud-based disaster recovery for databases

Authors:

Joel Alcântara,

Tiago Oliveira,

Alysson BessaniAuthors Info & Claims

Middleware '17: Proceedings of the 18th ACM/IFIP/USENIX Middleware Conference

Pages 248 - 260

https://doi.org/10.1145/3135974.3135985

Published: 11 December 2017 Publication History

Abstract

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 x 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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Han WXue JZhang FSun Z(2020)An Effective Remote Data Disaster Recovery Plan for the Space TT&C SystemMachine Learning for Cyber Security10.1007/978-3-030-62460-6_4(31-41)Online publication date: 8-Oct-2020
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Index Terms

Ginja: one-dollar cloud-based disaster recovery for databases
1. Computer systems organization
  1. Dependable and fault-tolerant systems and networks
2. Information systems
  1. Data management systems
    1. Middleware for databases
      1. Data replication tools

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cover image ACM Conferences

Middleware '17: Proceedings of the 18th ACM/IFIP/USENIX Middleware Conference

December 2017

268 pages

ISBN:9781450347204

DOI:10.1145/3135974

General Chairs:
K. R. Jayaram
IBM T. J. Watson Research Center
,
Anshul Gandhi
Stony Brook University
,
Program Chairs:
Bettina Kemme
McGill University
,
Peter Pietzuch
Imperial College London

Copyright © 2017 ACM.

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Published: 11 December 2017

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Middleware '17: 18th International Middleware Conference

December 11 - 15, 2017

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Middleware '17 Paper Acceptance Rate 20 of 85 submissions, 24%;

Overall Acceptance Rate 203 of 948 submissions, 21%

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Cited By

Karanjai RXu LChen LDiallo NShi WHong JPark J(2024)Decentralized FaaS over Multi-Clouds with Blockchain based Management for Supporting Emerging ApplicationsProceedings of the 39th ACM/SIGAPP Symposium on Applied Computing10.1145/3605098.3636029(122-130)Online publication date: 8-Apr-2024
https://dl.acm.org/doi/10.1145/3605098.3636029
Cao RTang ZLiu CVeeravalli B(2020)A Scalable Multicloud Storage Architecture for Cloud-Supported Medical Internet of ThingsIEEE Internet of Things Journal10.1109/JIOT.2019.29462967:3(1641-1654)Online publication date: Mar-2020
https://doi.org/10.1109/JIOT.2019.2946296
Han WXue JZhang FSun Z(2020)An Effective Remote Data Disaster Recovery Plan for the Space TT&C SystemMachine Learning for Cyber Security10.1007/978-3-030-62460-6_4(31-41)Online publication date: 8-Oct-2020
https://dl.acm.org/doi/10.1007/978-3-030-62460-6_4
Shen JLi YZhou YWang X(2019)Understanding I/O performance of IPFS storageProceedings of the International Symposium on Quality of Service10.1145/3326285.3329052(1-10)Online publication date: 24-Jun-2019
https://dl.acm.org/doi/10.1145/3326285.3329052
W. M. Ribeiro MA. B. Lima Ade Oliveira D(2019)OLAP parallel query processing in clouds with C‐ParGRESConcurrency and Computation: Practice and Experience10.1002/cpe.559032:7Online publication date: 19-Dec-2019
https://doi.org/10.1002/cpe.5590

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