Abstract
Cloud storage solutions promise high scalability and low cost. Existing solutions, however, differ in the degree of consistency they provide. Our experience using such systems indicates that there is a non-trivial trade-off between cost, consistency and availability. High consistency implies high cost per transaction and, in some situations, reduced availability. Low consistency is cheaper but it might result in higher operational cost because of, e.g., overselling of products in a Web shop.
In this paper, we present a new transaction paradigm, that not only allows designers to define the consistency guarantees on the data instead at the transaction level, but also allows to automatically switch consistency guarantees at runtime. We present a number of techniques that let the system dynamically adapt the consistency level by monitoring the data and/or gathering temporal statistics of the data. We demonstrate the feasibility and potential of the ideas through extensive experiments on a first prototype implemented on Amazon's S3 and running the TPC-W benchmark. Our experiments indicate that the adaptive strategies presented in the paper result in a significant reduction in response time and costs including the cost penalties of inconsistencies.
- 28msec, Inc. Sausalito. http://sausalito.28msec.com, Feb. 2009.Google Scholar
- Amazon. Simple Storage Service S3, Dec. 2008. http://aws.amazon.com/s3/.Google Scholar
- D. Barbará and H. Garcia-Molina. The Demarcation Protocol: A Technique for Maintaining Constraints in Distributed Database Systems. VLDB J., 3(3):325--353, 1994. Google ScholarDigital Library
- H. Berenson et al. A critique of ANSI SQL isolation levels. In Proc. of ACM SIGMOD, pages 1--10, Jun 1995. Google ScholarDigital Library
- P. Bernstein, V. Hadzilacos, and N. Goodman. Concurrency Control and Recovery in Database Systems. Addison Wesley, 1987. Google ScholarDigital Library
- M. Brantner, D. Florescu, D. A. Graf, D. Kossmann, and T. Kraska. Building a Database in the Cloud. http://www.dbis.ethz.ch/research/publications. Technical Report, ETH Zurich, 2009.Google Scholar
- M. Brantner, D. Florescu, D. A. Graf, D. Kossmann, and T. Kraska. Building a database on S3. In Proc. of ACM SIGMOD, pages 251--264, 2008. Google ScholarDigital Library
- M. Cafarella et al. Data management projects at google. ACM SIGMOD Record, 37(1):34--38, 2008. Google ScholarDigital Library
- F. Chang et al. Bigtable: A Distributed Storage System for Structured Data. In Proc. of OSDI, pages 205--218, 2006. Google ScholarDigital Library
- S. S. Chawathe, H. Garcia-Molina, and J. Widom. Flexible Constraint Management for Autonomous Distributed Databases. IEEE Data Eng. Bull., 17(2):23--27, 1994.Google Scholar
- B. F. Cooper et al. PNUTS: Yahoo!'s hosted data serving platform. In Proc. of VLDB, volume 1, pages 1277--1288, 2008. Google ScholarDigital Library
- L. Gao, M. Dahlin, A. Nayate, J. Zheng, and A. Iyengar. Application specific data replication for edge services. In Proc. of WWW, pages 449--460, 2003. Google ScholarDigital Library
- D. Gawlick and D. Kinkade. Varieties of Concurrency Control in IMS/VS Fast Path. IEEE Database Eng. Bull., 8(2):3--10, 1985.Google Scholar
- J. Gray and A. Reuter. Transaction Processing: Concepts and Techniques. Morgan Kaufmann, 1994. Google ScholarDigital Library
- J. Gray, P. Sundaresan, S. Englert, K. Baclawski, and P. J. Weinberger. Quickly generating billion-record synthetic databases. In Proc. of ACM SIGMOD, pages 243--252, 1994. Google ScholarDigital Library
- H. Guo, P.-Å. Larson, and R. Ramakrishnan. Caching with 'good enough' currency, consistency, and completeness. In VLDB, pages 457--468, 2005. Google ScholarDigital Library
- H. Guo, P.-Å. Larson, R. Ramakrishnan, and J. Goldstein. Relaxed Currency and Consistency: How to Say "Good Enough" in SQL. In Proc. of ACM SIGMOD, pages 815--826, 2004. Google ScholarDigital Library
- A. Gupta and S. Tiwari. Distributed constraint management for collaborative engineering databases. In CIKM, pages 655--664, 1993. Google ScholarDigital Library
- P.-Å. Larson, J. Goldstein, and J. Zhou. MTCache: Transparent Mid-Tier Database Caching in SQL Server. In Proc. of ICDE, pages 177--189, 2004. Google ScholarDigital Library
- J. Lee. SQL Data Services - Developer Focus (Whitepaper). http://www.microsoft.com/azure/data.mspx, 2008.Google Scholar
- Y. Lu, Y. Lu, and H. Jiang. Adaptive Consistency Guarantees for Large-Scale Replicated Services. In Proc. of NAS, pages 89--96, Washington, DC, USA, 2008. IEEE Computer Society. Google ScholarDigital Library
- C. Olston, B. T. Loo, and J. Widom. Adaptive Precision Setting for Cached Approximate Values. In SIGMOD Conference, pages 355--366, 2001. Google ScholarDigital Library
- P. E. O'Neil. The Escrow Transactional Method. TODS, 11(4):405--430, 1986. Google ScholarDigital Library
- M. T. Ozsu and P. Valduriez. Principles of Distributed Database Systems. Prentice Hall, 1999. Google ScholarDigital Library
- R. Kallman et all. H-store: a high-performance, distributed main memory transaction processing system. In Proc. of VLDB, pages 1496--1499, 2008. Google ScholarDigital Library
- S. Shah, K. Ramamritham, and P. J. Shenoy. Resilient and Coherence Preserving Dissemination of Dynamic Data Using Cooperating Peers. IEEE Trans. Knowl. Data Eng., 16(7):799--812, 2004. Google ScholarDigital Library
- E. A. Silver, D. F. Pyke, and R. Peterson. Inventory Management and Production Planning and Scheduling. Wiley, 3 edition, 1998.Google Scholar
- M. Stonebraker et al. Mariposa: A Wide-Area Distributed Database System. VLDB J., 5(1), 1996. Google ScholarDigital Library
- M. Stonebraker et al. The end of an architectural era (it's time for a complete rewrite). In Proc. of VLDB, pages 1150--1160, 2007. Google ScholarDigital Library
- A. T. Tai and J. F. Meyer. Performability Management in Distributed Database Systems: An Adaptive Concurrency Control Protocol. In Proc. of MASCOTS, page 212, 1996. Google ScholarDigital Library
- A. Tanenbaum and M. van Steen. Distributed Systems: Principles and Paradigms. Prentice Hall, 2002. Google ScholarDigital Library
- TPC. TPC-W Benchmark 1.8. TPC Council, 2002.Google Scholar
- W. Vogels. Data access patterns in the Amazon.com technology platform. In Proc. of VLDB, page 1, Sep 2007. Google ScholarDigital Library
- G. Weikum and G. Vossen. Transactional Information Systems. Morgan Kaufmann, 2002. Google ScholarDigital Library
- A. Yalamanchi and D. Gawlick. Compensation-Aware Data types in RDBMS, 2009. To appear in Proc. of ACM SIGMOD. Google ScholarDigital Library
- H. Yu and A. Vahdat. Design and Evaluation of a Continuous Consistency Model for Replicated Services. In OSDI, pages 305--318, 2000. Google ScholarDigital Library
Index Terms
- Consistency rationing in the cloud: pay only when it matters
Recommendations
On Mixing Eventual and Strong Consistency: Acute Cloud Types
In this article we study the properties of distributed systems that mix eventual and strong consistency. We formalize such systems through <italic>acute cloud types</italic> (ACTs), abstractions similar to conflict-free replicated data types (CRDTs), ...
Cost-Based Data Consistency in a Data-as-a-Service Cloud Environment
CLOUD '12: Proceedings of the 2012 IEEE Fifth International Conference on Cloud ComputingClouds are becoming the preferred platforms for large-scale applications. Currently, Cloud environments focus on high scalability and availability by relaxing consistency. Weak consistency's considered to be sufficient for most of the currently deployed ...
Strict Timed Causal Consistency as a Hybrid Consistency Model in the Cloud Environment
AbstractCloud computing is a model of distributed systems. This system allows users to access virtual resources including the processing power, storage, applications, etc. Storage as a Service (SaaS) is one of the cloud computing services. ...
Highlights- Cloud storage systems provide the storage service for the end-users.
- Cloud ...
Comments