skip to main content
10.1145/2851613.2851641acmconferencesArticle/Chapter ViewAbstractPublication PagessacConference Proceedingsconference-collections
research-article

Dynamic adaptation of geo-replicated CRDTs

Published: 04 April 2016 Publication History

Abstract

Conflict-free Replicated Data Types (CRDTs) are high-level data types that can be replicated with minimal coordination among replicas due to its confluent semantics. This property makes CRDTs specially appealing for geo-replicated settings. Different approaches, such as state transfer and operation forwarding, have been proposed to propagate updates among replicas, with different tradeoffs among the amount of network traffic generated and the staleness of local information. This paper proposes and evaluates techniques to automatically adapt a CRDT implementation, such that the best approach is used, based on the application needs (captured by a SLA) and the observed system configuration. Our techniques have been integrated in SwiftCloud, a state of the art geo-replicated store based on CRDTs.

References

[1]
P. Almeida, A. Shoker, and C. Baquero. Efficient state-based CRDTs by delta-mutation. CoRR, abs/1410.2803, 2014.
[2]
C. Baquero, P. Almeida, and A. Shoker. Making operation-based CRDTs operation-based. In K. Magoutis and P. Pietzuch, editors, Distributed Applications and Interoperable Systems, LNCS, pages 126--140. Springer Berlin Heidelberg, 2014.
[3]
A. Bieniusa, M. Zawirski, N. Preguiça, M. Shapiro, C. Baquero, V. Balegas, and S. Duarte. An optimized conflict-free replicated set. CoRR, abs/1210.3368, 2012.
[4]
B. Cooper, A. Silberstein, E. Tam, R. Ramakrishnan, and R. Sears. Benchmarking cloud serving systems with YCSB. In SoCC, 2010.
[5]
M. Couceiro, G. Chandrasekara, M. Bravo, M. Hiltunen, P. Romano, and L. Rodrigues. Q-opt: Self-tuning quorum system for strongly consistent software defined storage. In Middleware '15, Vancouver, BC, Canada, 2015.
[6]
V. Hodge and J. Austin. A survey of outlier detection methodologies. Artificial Intelligence Review, 22(2):85--126, 2004.
[7]
R. Kalman. A new approach to linear filtering and prediction problems. Journal of Fluids Engineering, 82(1):35--45, 1960.
[8]
L. Lamport. The part-time parliament. ACM Trans. Comput. Syst., 16(2):133--169, May 1998.
[9]
M. Letia, N. Preguiça, and M. Shapiro. CRDTs: Consistency without concurrency control. CoRR, abs/0907.0929, 2009.
[10]
W. Lloyd, M. Freedman, M. Kaminsky, and D. Andersen. Don't settle for eventual: Scalable causal consistency for wide-area storage with COPS. In SOSP, 2011.
[11]
A. Metwally, D. Agrawal, and A. El Abbadi. Efficient computation of frequent and top-k elements in data streams. In ICDT, volume 3363 of LNCS, pages 398--412. Springer, 2005.
[12]
D. Navalho, S. Duarte, N. Preguiça, and M. Shapiro. Incremental stream processing using computational conflict-free replicated data types. CloudDP, 2013.
[13]
E. Page. Continuous inspection schemes. Biometrika, 41(1/2):pp. 100--115, 1954.
[14]
N. Preguiça, J. Marques, M. Shapiro, and M. Letia. A commutative replicated data type for cooperative editing. In ICDCS, 2009.
[15]
M. Shapiro, N. Preguiça, C. Baquero, and M. Zawirski. A comprehensive study of Convergent and Commutative Replicated Data Types. Research Report RR-7506, 2011.
[16]
M. Shapiro, N. Preguiça, C. Baquero, and M. Zawirski. Conflict-free replicated data types. SSS, 2011.
[17]
D. Terry, A. Demers, K. Petersen, M. Spreitzer, M. Theimer, and B. Welch. Session guarantees for weakly consistent replicated data. In PDIS, 1994.
[18]
D. Terry, V. Prabhakaran, R. Kotla, M. Balakrishnan, M. Aguilera, and H. Abu-Libdeh. Consistency-based service level agreements for cloud storage. SOSP, 2013.
[19]
W. Vogels. Eventually consistent. Commun. ACM, 52(1):40--44, Jan. 2009.
[20]
M. Zawirski, N. Preguiça, S. Duarte, A. Bieniusa, V. Balegas, and M. Shapiro. Write fast, read in the past: Causal consistency for client-side applications. Middleware, 2015.

Cited By

View all

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
SAC '16: Proceedings of the 31st Annual ACM Symposium on Applied Computing
April 2016
2360 pages
ISBN:9781450337397
DOI:10.1145/2851613
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 04 April 2016

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. CRDTs
  2. geo-replication
  3. operation-based
  4. state-based

Qualifiers

  • Research-article

Funding Sources

Conference

SAC 2016
Sponsor:
SAC 2016: Symposium on Applied Computing
April 4 - 8, 2016
Pisa, Italy

Acceptance Rates

SAC '16 Paper Acceptance Rate 252 of 1,047 submissions, 24%;
Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)4
  • Downloads (Last 6 weeks)0
Reflects downloads up to 06 Nov 2024

Other Metrics

Citations

Cited By

View all

View Options

Get Access

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media