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Data Management in the Cloud “Consistency Rationing in the Cloud: Pay Only When It Matters” Authors: Tim Kraska , Martin Hentschel , Gustavo Alonso, and Donald Kossmann VLDB ’09, August. Latasha A. Gibbs CSCE 824 – Secure Database Systems Spring 2013 University of South Carolina. AGENDA. - PowerPoint PPT Presentation
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Data Management in the Cloud
“Consistency Rationing in the Cloud: Pay Only When It Matters”
Authors: Tim Kraska, Martin Hentschel, Gustavo Alonso, and Donald KossmannVLDB ’09, August
Latasha A. GibbsCSCE 824 – Secure Database Systems
Spring 2013University of South Carolina
AGENDA• INTRODUCTION• USE CASES• CONSISTENCY RATIONING• POLICIES• IMPLEMENTATION• SUMMARY• FUTURE WORK & QUESTIONS
INTRODUCTION
• Promise of high scalability and low cost• Existing solutions differ in the level of consistency provided• Implement database-like facilities on top of cloud storage• High consistency means high cost per transaction• Lower consistency is cheaper• Not all data needs to be treated with the same level of consistency
AT WHAT PRICE?
• CONSISTENCY LEVEL is measured in terms of the number (#) of service calls needed to enforce CONSISTENCY LEVEL
A & B• Category A – Serializability • Expensive in in terms of
monetary costs and performance• Serializability is provided via 2PL• Data should be put in Category A
when up-to-date views are a must
• Category B – Adaptive • Level of consistency depends on
situation• Switches between session
consistency and serializability at runtime• Policies are designed to make
the switch automatic and dynamic
C• Category C – Session
Consistency has been identified as the minimum consistency level that does not result in excessive complexity for the developer• After some time the system will
converge and become eventually consistent
• Session consistency is cheap• Permits extensive caching• When inconsistencies cannot
occur, cloud databases should place data in C category
AGENDA• INTRODUCTION• USE CASES• CONSISTENCY RATIONING• POLICIES• IMPLEMENTATION• SUMMARY• FUTURE WORK & QUESTIONS
USE CASES CONTINUED…
• Strategy based on update frequency• Selection of consistency protocol is based on the likelihood of
conflicts• Parts of the document that are updated frequently would be handled
by strong consistency guarantees in for instance (Category A)
Collaborative Editing
CONSISTENCY RATIONING?Since strong consistency is expensive…
1. Use the analysis of categories A, B, and C to categorize the data2. Apply different consistency strategies for each category
INCONSISTENCY
COSTTRANSACTION COST
AGENDA• INTRODUCTION• USE CASES• CONSISTENCY RATIONING• POLICIES• IMPLEMENTATION• SUMMARY• FUTURE WORK & QUESTIONS
POLICIESFive different policies are created to adapt the consistency guarantees for data items in Category B
• General Policy• Time Policy• Fixed Threshold Policy• Demarcation Policy• Dynamic Policy
GENERAL POLICY• Works on the basis of conflict probability• Looks into the probability of conflict on a given data item and
switches to serializability if probability is high enough• Probability of conflicting update on a record is given by the formula
below:
AGENDA• INTRODUCTION• USE CASES• CONSISTENCY RATIONING• POLICIES• IMPLEMENTATION• SUMMARY• FUTURE WORK & QUESTIONS
IMPLEMENTATION
16
BASIC PROTOCOL
HOW IS DATA ABOUT DATA USED?
• Each collection contains meta data about its type• Given the collection a record belongs to, the system checks to see
which consistency level should be enforced• For example, if a record is classified as A data, serializability with
strong guarantees is performed• For B data, meta data contains the name of the policy and additional
parameters
EXPERIMENTS• Database hosted on S3, clients connect to the database via
applications servers that run on Amazon’s EC2• Based on the TPC-W benchmark• Relax the requirement for strong consistency guarantees• All experiments were scheduled to run for 300 seconds and were
repeated 10x• Consistency categories were assigned to the data types of the TPC-W
benchmark for A data, C data, and Mixed data
OPTIMIZATION
PENALTIES
AGENDA• INTRODUCTION• USE CASES• CONSISTENCY RATIONING• POLICIES• IMPLEMENTATION• SUMMARY• FUTURE WORK & QUESTIONS
SUMMARY• It is possible to assign a very precise monetary cost to consistency
protocols• Optimization is based on allowing the database to exhibit
inconsistencies if it helps to reduce the cost of a transaction and does not cause higher penalty costs• Consistency Rationing lowers overall cost and improves performance
in cloud-based database systems• Step towards probabilistic consistency guarantees…
“One small step for man, one giant leap for mankind.” –Neil Armstrong
FUTURE OUTLOOK
• Faster statistical methods• Automatic optimization• New policies• Implementation on other
platforms• Emergency rationing
REFERENCES[1] Abadi, Daniel, J. “Data Management in the Cloud: Limitations and Opportunities”. In IEEE Data Engineering Bulletin, 32 (1) Yale University. 2009.[2] Coy, Steven P. “Security Implications of the Choice of Distributed Database Management System Model: Relational vs. Object-Oriented”. University of Maryland. 2008.[3] Niccolai, James. “Four Companies Rethink Databases for the Cloud”. Computer World. 23 June. 2011. Web. 16 February 2013.[4] Abbadi, Amr El, Agrawal, Divyakant, and Das, Sudipto. “Big Data and Cloud Computing: Current State and Future Opportunities”. In the Proceedings of EDBT 2011, ACM March 22-24, 2011.[5] Valduriez, Patrick. “Principles of Distributed Data Management in 2020?”. DEXA’11 In the Proceedings of the 22nd International Conference on Database and Expert Systems Applications. Volume 1.[6] Lu, Yanbin and Tsudik, Gene. “Privacy-Preserving Cloud Database Querying”. In the Journal of Internet Services and Information Security (JISIS). Vol.1. No. 4, November 2011.[7] http://www.hadoop.apahe.org/[8] http://www.relationalcloud.com/[9] Curino, C., Jones, E., Popa, R., Malviya, N., Wu, E. Madden, S., Balakrishnan, H., and Zeldovich, N. “Relational Cloud: A Database-as-a-Service for the Cloud”. In the Proceedings of the 5th Biennial Conference on Innovative Data Systems Research. January 2011.[10] Özsu, M. Tamer and Valduriez, Patrick. Principles of Distributed Database Systems. New York: Pearson Education, Inc., 2011. Print.[11] Alonso, Gustavo, Hentschel, Martin, Kossmann, Donald, and Kraska, Tim, “Consistency Rationing: Pay Only When It Matters”. In the Proceedings of International Conference of Very Large Databases (VLDB). 2009.
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