1. UNIT: User-ceNtrIc Transaction Management in Web-Database Systems Huiming Qu, Alexandros Labrinidis, Daniel Mosse Advanced Data Management Technologies Lab http://db.cs.pitt.edu Department of Computer Science University of Pittsburgh

2. ADMT Lab, Department of Computer Science, University of Pittsburgh 2 UPDATESQUERIES Stock Trading Services (ideal) Web databases GOOG IBM $367.9 $75.8 GOOG IBM

3. ADMT Lab, Department of Computer Science, University of Pittsburgh 3 GOOG Stock Trading Services (reality) Web databases GOOG IBM SUN GOOG MSFT GOOG IBM OTE OVERLOADED! To avoid overloading: 1.increase hardware capacity, or 2.adding software support

4. ADMT Lab, Department of Computer Science, University of Pittsburgh 4 Stock Trading Services (UNIT) Web databases UNIT MSFT GOOG IBM SUN TUTU $367.9 $75.8 GOOG IBM OTE

5. ADMT Lab, Department of Computer Science, University of Pittsburgh 5 Problem Statement Users’ satisfaction are based on: Freshness: query is answered based on fresh data Timeliness: query is answered with short response time Transaction types –read-only queries and write-only updates are competing for system resources, more cpu to queries, better timeliness. more cpu to updates, better freshness. Optimization Goal: Maximize user satisfaction –through balancing the load of query and update transactions.

6. ADMT Lab, Department of Computer Science, University of Pittsburgh 6 Outline Motivating Example Performance metric: User Satisfaction System overview & algorithms Experiments Related work Conclusions

7. ADMT Lab, Department of Computer Science, University of Pittsburgh 7 User Requirements Timeliness: Meeting deadlines –Query response time ≤ its relative deadline. Freshness: Meeting freshness requirements –Query freshness ≥ its freshness requirement. –Query freshness (aggregation of data freshness): The minimal freshness of data accessed by the query –Data freshness (lag-based): Based on the number of unapplied updates Query U1 U3 U2 Q1 returns with U1 t

8. ADMT Lab, Department of Computer Science, University of Pittsburgh 8 Is Success Ratio Enough? Queries may be failed and dropped if: –rejected because of the admission control (Rejection Failure), or –fail to meet the deadlines (Deadline Missed Failure), or –fail to meet the freshness requirements (Data Stale Failure) Otherwise, it succeeds. Success Ratio: % of queries meeting their timeliness and freshness requirements. What is missing from success ratio? –Users’ preferences between timeliness and freshness.

9. ADMT Lab, Department of Computer Science, University of Pittsburgh 9 User Satisfaction Metric (USM)

10. ADMT Lab, Department of Computer Science, University of Pittsburgh 10 Outline Motivating Example Performance metric: User Satisfaction System overview & algorithms Experiments Related work Conclusions

11. ADMT Lab, Department of Computer Science, University of Pittsburgh 11 UNIT System (User-ceNtrIc Trans- action Management) Web-databases –Dual priority queue Updates > queries EDF for queries FIFO for updates –2PL-HP UNIT: load control –Load Balancing Controller –Query Admission Control –Update Frequency Modulation USM Load Balancing Controller Queries Data Reject Failure Deadline Missed Failure Success Data Stale Failure UNIT +/- queries Admission Control Frequency Modulation Updates Statistics +/- updates

12. ADMT Lab, Department of Computer Science, University of Pittsburgh 12 Gain Load Balancing Controller Success Gain Failure Cost Gain Rejection Cost Deadline Missed Cost Data Stale Cost Increase # of queries Decrease # of updates Decrease # of queries Increase # of updates + - Rejection Cost Data Stale Cost Deadline Missed Cost 0

13. ADMT Lab, Department of Computer Science, University of Pittsburgh 13 Query Admission Control Transaction deadline check –Will query meet its deadline with the current system workload? System USM check –Will query jeopardize the system USM if admitted? time q1q2q3q5 q6q7 q4 q5-7 deadlines Current time q6q7 q4 deadline

14. ADMT Lab, Department of Computer Science, University of Pittsburgh 14 Query Admission Control (cont.) Use C flex to Increase/Decrease # of queries –Decrease C flex to increase queries admitted –Increase C flex to decrease queries admitted time q1q2q3 q5-7 deadlines Current time q6q7q5 q4 deadline q1q2q3q1q2q3 smaller C flex larger C flex C flex

15. ADMT Lab, Department of Computer Science, University of Pittsburgh 15 Update Frequency Modulation Decrease # of Updates –Ticket Value (TV) for each active data item. –Updates increase TV; Queries decrease TV. –Higher TV  higher probability to be degraded. –Lottery Scheme [Waldspurger 95] to pick data items to drop updates. Increase # of Updates –Randomly pick a degraded data item. –Restore all its updates. D1D2 D3 U1Q3U1 D1 is picked to reduce its updates!

16. ADMT Lab, Department of Computer Science, University of Pittsburgh 16 Outline Motivating Example Performance Metric: User Satisfaction System Overview & Algorithms Experiments Related Work Conclusions

17. ADMT Lab, Department of Computer Science, University of Pittsburgh 17 Algorithms Compared IMU –I–Immediate Update, no admission control, 100% freshness ODU –O–On-demand Update, no admission control, 100% freshness QMF: [Kang,TKDE’04] –I–Immediate update, admission control, no weights among rejection, timeliness and freshness requirements are considered. UNIT –i–is what U need

18. ADMT Lab, Department of Computer Science, University of Pittsburgh 18 Experiment Design We want to evaluate the following: 1.Effectiveness of the update frequency modulation, 2.Performance under the naïve USM setting (= Success Ratio), 3.Performance under various USM settings, 4.Distribution of four query outcomes under various USM settings.

19. ADMT Lab, Department of Computer Science, University of Pittsburgh 19 Experimental Setup Query trace –based on HP disk cello99a access traces (1069 hours, 110,035 reads). Relative deadline generated from query exec time qt –uniformly distributed from avg(qt) to 10 * max(qt)). Freshness requirement for all queries is set to 90%. Update traces update traces workloadCorrelation to queries low-uniflow (6144, 15%) Uniform low-posPositive low-negNegative med-unifmed (30000, 75%) Uniform med-posPositive med-negNegative high-unifhigh (61440, 150%) Uniform high-posPositive high-negNegative

20. ADMT Lab, Department of Computer Science, University of Pittsburgh 20 1. Update Frequency Modulation Evaluation Query Distributions on DataUpdate Distributions on Data (med-unif) few queries Updates can be removed without hurting query freshness.

21. ADMT Lab, Department of Computer Science, University of Pittsburgh 21 1. Update Frequency Modulation Evaluation (cont.) Query Distributions on DataUpdate Distributions on Data (med-neg) few queries A very small portion of updates are needed to keep queries freshness high.

22. ADMT Lab, Department of Computer Science, University of Pittsburgh 22 UNIT has the least performance drop when workload increases. 2. Naïve USM = Success Ratio (gain = 1, penalties = 0) positive correlationnegative correlation

23. ADMT Lab, Department of Computer Science, University of Pittsburgh 23 3. USM (gain = 1, penalties ≠ 0) Case 1 - Gain dominates: penalties = 0.1 or 0.5 Case 2 - Penalty dominates: penalties = 1 or 5 UNIT has the least penalties. UNIT has the highest gain.

24. ADMT Lab, Department of Computer Science, University of Pittsburgh 24 4. Query outcome distributions UNIT obtains higher success ratio than others because it keeps queries from falling into the categories that have higher penalties. Percentage of queries that are rejected (R), failed to meet deadlines (D), failed to meet freshness (F), or succeed (S). Other Algorithms UNIT under different USM settings

25. ADMT Lab, Department of Computer Science, University of Pittsburgh 25 Related work Web-databases –[Luo et al. Sigmod 02] –[Datta et al. Sigmod 02] –[Challenger et al. Infocom 00] –[Labrinidis et al. VLDBJ 04] –… Real time databases –[Adelberg et al., Sigmod 95] –[Kang et al., TDKE 04] –… Stream Processing –[Tatbul et al., VLDB 03] –[Das et al., Sigmod 03] –[Ding et al., CIKM 04] –[Babcock et al., ICDE 04] –[Sharaf et al., WebDB 05] –…

26. ADMT Lab, Department of Computer Science, University of Pittsburgh 26 Outline Motivating Example Performance metric: User Satisfaction System overview & algorithms Experiments Related work Conclusions

27. ADMT Lab, Department of Computer Science, University of Pittsburgh 27 Conclusions We proposed –a unified User Satisfaction Metric (USM) for web-database systems, –a feedback control system, UNIT, to control the query and update workload in order to maximize system USM, and –two algorithms that perform query admission control and update frequency modulation to balance the query and update workload. We finally showed with extensive simulation study based on real data that UNIT outperforms two baseline algorithms and the current state of the art.

28. Thank you! Questions and Comments Huiming Qu huiming@cs.pitt.edu

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32. ADMT Lab, Department of Computer Science, University of Pittsburgh 32 User Requirements Timeliness: Meeting deadlines Freshness: Meeting freshness requirements

33. ADMT Lab, Department of Computer Science, University of Pittsburgh 33 Performance Metrics Timeliness –response time Freshness –time-based (t) –divergence-based (50) –lag-based (2) –… Deficiency of the above traditional metrics is –Lack of semantic info (user preferences/requirements) from applications. U1:$300U3:$350 t U2:$310 Q1 returns with U1:$300

34. ADMT Lab, Department of Computer Science, University of Pittsburgh 34 Update Frequency Modulation Degrade Update –Each data item maintains a Degrading Ticket Value T j –Lottery Schemes [Waldspurger 95], higher ticket value means more probably to be degraded. –Query decrease T j by DT j, Update increase T j by IT j –If picked, it is degraded by 10%. Upgrade Update –randomly pick a degraded data item –Upgrade it by 50%

35. ADMT Lab, Department of Computer Science, University of Pittsburgh 35 UNIT outperforms others in all cases. Naïve USM