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Guiding Personal Choices in a Quality Contracts Driven Query Economy

Guiding Personal Choices in a Quality Contracts Driven Query Economy. Huming Qu 1 , Jie Xu 2 , Alexandros Labrinidis 2 1 IBM Watson Research Center 2 University of Pittsburgh. Audience Questions.

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Guiding Personal Choices in a Quality Contracts Driven Query Economy

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  1. Guiding Personal Choices in a Quality Contracts Driven Query Economy Huming Qu1, Jie Xu2, Alexandros Labrinidis2 1 IBM Watson Research Center 2 University of Pittsburgh

  2. Audience Questions • Question #1How many of you consider the time to get an airfare quote from a travel web site too long (sometimes)? • Question #2How many of you clicked on an airfare at a travel web site, only to get a message saying “price changed”?

  3. QoS vs. QoD Trade-off best QoD What if you could specify your preferences (on the trade-off between QoS and QoD)? worst worst best QoS

  4. Roadmap Conclusions % of audience asleep Experiments AQC Algorithm Background Motivation # of slides

  5. Application Architecture WWW Browser WWW Server Queries Web-database Updates Data Warehouse

  6. Application Architecture WWW Browser • Web-DB workloads • Read-only queries • Write-only updates • Intensive and/or bursty • QoS vs. QoD • Quality of Service • Answer queriesfast • Quality of Data • Finish updates on time • At trade-off with each other WWW Server Queries Web-database Updates User preferences can help system with resource allocation Data Warehouse

  7. Users care about Timeliness and Staleness Why scheduling? • Impact of scheduling • A simple test • FIFO • FIFO-UH (Update High) • FIFO-QH (Query High) • Nonebest on both dimensions • Combining performance metrics • Set constraint on one metric and optimize another [Kang04] • Construct a single metric based on weighted aggregation [Abadi05]

  8. Quality Contracts (QC) • Combine performance metrics • Convert incomparable QoS and QoD to the common “worth” to users • Capture user preference • Among quality metrics • Among different queries worth ∑ worth= $8 quality metric Response time = 30ms

  9. Related work User preferences • Grid computing • [AuYoung, et al., 2006] • [Buyya et al., 2005] • [Wolski et al., 2001] • … • Distributed databases • [Braumandl et al., 2003] • [Benatallah et al., 2002] • [Naumann et al., 1999] • … • Web-databases • [Challenger et al. 2000] • [Luo et al. 2002] • [Datta et al. 2002] • [Labrinidis et al. 2004] • [Qu et al. 2006] • [Labrinidis et al. 2006] • [Guirguis et al. 2009] • … • Real time systems • [Abbott et al., 1988] • [Sha et al., 1991] • [Haritsa et al., 1993] • [Ramamritham et al., 1994] • [Adelberg et al., 1996] • [Burns et al., 2000] • … • Stream Processing • [Carney et al., 2002] • [Das et al., 2003] • [Babcock et al., 2004] • [Sharaf et al., 2005] • [Abadi et al., 2005] • [Sharaf et al., 2008] • … • Economic Models • [Ferguson et al., 1996] • [Stonebraker et al., 1996] • …

  10. Roadmap Conclusions % of audience asleep Experiments AQC Algorithm Background Motivation # of slides

  11. User side – Problem definition • Given • Total budget B, Total queries N • Objective • Maximize Success Ratio (query result worth > 0, or Qpaid> 0) • QC setup • Known: • rtmax, • uumax, • qosmax/qodmax • Unknown:Qmax = qosmax + qodmax • Problem • How to adapt Qmaxto maximize Success Ratio?

  12. Baseline Algorithms • Fixed (FIX) • Fixed average • Pro: simple, once for all • Con: ignores the refund comes from previous failures • Random (RAN) • Random based on fixed average • Pro: simple • Con: ignores the refund comes from previous failures • Dynamic (DYN) • Future average • Pro: keep an eye on the budget left and queries left • Con: future average keeps increasing, money distributed unevenly

  13. Example($10 budget per query) FIX DYN $10 $10 $10 Future average (DYN) Unfair distribution of the budget Fixed average (FIX, RAN) Not fully make use of the budget time time RAN time

  14. Adaptive Quality Contract (AQC) • Overbid -bid more than you can afford • Deposit- bid less when continuous successes occur AQC Mode Selection If failureQ.size> 0 Overbid Mode else if successQ.size>c Deposit Mode

  15. Adaptive Quality Contract (AQC) • Overbid • Goal: make full use of the budget to boost the query priority • DYN : • AQC : Solve for

  16. Probability of returning before rtmax Percentage of returning before rtmax AQC – Overbid w/ Linear QC • Getting expected payment from QoS function S(x) S(1) = 5 Empirical Expectation

  17. AQC – Overbid • Overbid • Make use of entire budget • But server performance is affected by behaviors of all users smaller than 1

  18. AQC – Deposit • Deposit • Goal: saving money in a less competitive environment to be ready for more competitive one • Decrease of is proportional to qosmax = $10 qospaid = $8 20ms qospaid = $1

  19. Roadmap Conclusions % of audience asleep Experiments AQC Algorithm Background Motivation # of slides

  20. Experimental Setup • Real stock web site traces on April 24, 2000 • # queries: 120,000 • # updates: 396,000 • # stocks: 4,107 • Budget per query $10 • Four classes of users: FIX, RAN, DYN, AQC

  21. Experiment Design • Performance • 1-class experiment: FIX, RAN, DYN, AQC • 2-class experiment: FIX-AQC, RAN-AQC, DYN-AQC • Population • Evaluate various population of users using different algorithms • Knowledge scope • Evaluate various users’ knowledge scope of other users’ information

  22. AQC beats other strategy up to 3X! Success Ratio:1-class, 2-class 1-class 2-class

  23. AQC makes fully use of user budget! Over time:1-class FIX DYN $10 $10 $10 $10 time time RAN AQC time time

  24. More competitive users decreases overall success ratio Population • Mixing AQC & RAN users with different population • AQC 10%, RAN 90% • AQC 50%, RAN 50% • AQC 90%, RAN 10%

  25. Sharing more information increases success ratio and reduce the risk Knowledge scope • 100,000 users; 1 ~ 10,000 number of groups • Group members share all history information

  26. Roadmap Conclusions % of audience asleep Experiments AQC Algorithm Background Motivation # of slides

  27. Would users go for this? • Implementation could be like cell-phone plans • Provide user with set of predetermined options to choose from • Examples: • have predetermined ratio of preference of QoS over QoD and vice-versa • have predetermined budget levels for each query (to choose from)

  28. Conclusions • Believe that utilizing user preferences on QoS/QoD can help system decide better under resource constraints • Presented (AQC) algorithm to help users dynamically adjust Quality Contracts of queries • Algorithm is dynamic and adapts to changing conditions of the “economy

  29. Questions? • More info:Advanced Data Management Technologies Lab (ADMT)http://db.cs.pitt.edu • Funding:National Science Foundation • Career award IIS-0746696 • ITR award ANI-0325353

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