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Preference-Aware Query and Update Scheduling in Web-databases

Preference-Aware Query and Update Scheduling in Web-databases. Huiming Qu Department of Computer Science University of Pittsburgh. Joint work with Prof. Alex Labrinidis. Applications Online stock info. Weather, news… Inventory monitoring Network monitoring Web-DB workloads

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Preference-Aware Query and Update Scheduling in Web-databases

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  1. Preference-Aware Query and Update Scheduling in Web-databases Huiming Qu Department of Computer Science University of Pittsburgh Joint work with Prof. Alex Labrinidis

  2. Applications Online stock info. Weather, news… Inventory monitoring Network monitoring Web-DB workloads Real-only queries Write-only updates Intensive and/or bursty We focus on scheduling queries and updates to enhance Web-Database performance Applications WWW Browser WWW Server Queries Web-database Updates Data Warehouse @HKUST, 12/18/2006

  3. Users care about Timeliness and Staleness Why scheduling? • Impact of scheduling • A simple test • FIFO • FIFO-UH (Update High) • FIFO-QH (Query High) • 2PL-HP [Abbott92] • Nobest for both dimensions • Combining performance metrics • Set constraint on one metric and optimize another [Kang04] • Construct a single metric based on weighted aggregation [Abadi05] @HKUST, 12/18/2006

  4. Outline • Introduction • Quality Contracts • Framework for unifying timeliness and staleness metrics through user preferences • QUTS • Query Update Time Share Scheduling according to Quality Contract framework • Experiments • Related work • Conclusions @HKUST, 12/18/2006

  5. Captures user preference Between quality metrics Among different queries Quality Contract Convert performance on individual metric to the “worth” to users Quality Contracts (QC) @HKUST, 12/18/2006

  6. Step QC QoS function QoD function Linear QC QoS function QoD function Quality Contract (QC) – cont. • Each Query is attached with a QC • monotonically non-increasing positive QC functions • QC<type, qosmax, rtmax, qodmax, uumax> • Optimization goal • system profit, P • More details in • [Labrinidis06] [Qu07] @HKUST, 12/18/2006

  7. Outline • Introduction • Quality Contracts • QUTS: Query Update Time Share Scheduling • Experiments • Related work • Conclusions @HKUST, 12/18/2006

  8. V RD Profit guided scheduling • Considers both value and constraint • Scheduling according to utility function in real time systems • VRD [haritsa93] • Value/constraint inflated priorities for queries and updates OK Incomparable @HKUST, 12/18/2006

  9. Baseline algorithms • Single priority queue • FIFO • w/ incomparablepriorities, queries and updatescannot be put together • Dual priority queue [adelberg95] • Separate update queue & query queue • Prioritize queries and updates correspondingly • UH (Update High) • QH (Query High) • Eliminate hard-wired preference for either QoS or QoD! • Profit-guided resource allocation (which queue to execute) @HKUST, 12/18/2006

  10. QUTS (Query Update Time Share) • Dual priority queue with dynamic concatenation • Two-level scheduling scheme (meta-scheduling) • Q1: how much CPU should be allocated to queries? • Q2: how to deploy CPU allocation? @HKUST, 12/18/2006

  11. Q1 - how much CPU to assign? • Query CPU percentage ρ (0 ≤ρ≤ 1) • Approximate system profit P using ρ QoS QoD Query share QoSmax profit QoDmax profit Update share Query share Quadratic function with linear constraints Optimal solution to maximize P @HKUST, 12/18/2006

  12. Q2 - how to deploy CPU allocation? • Periodically compute ρ and alleviate random variation • With in a single adaptation period , the percentage of CPU assigned to queries should be ρ. time @HKUST, 12/18/2006

  13. Q2 - how to deploy CPU allocation? • Adaptation period ωis divided to N atom time • For each , the system refreshes the system state according to ρ • Query High: • Update High: • Length of atom time • Too small → too many interruptions • Too large → miss opportunities … time ρ = 0.5 @HKUST, 12/18/2006

  14. Outline • Introduction • Quality Contracts • QUTS: Query Update Time Share Scheduling • Experiments • Related work • Conclusions @HKUST, 12/18/2006

  15. Experiment Setup • Real stock web site traces on April 24, 2000 • # queries: 82129 • # updates: 496892 • # stocks: 4602 • Scheduling algorithms compared through simulation @HKUST, 12/18/2006

  16. Experiment Design • Performance • Compare QUTS with FIFO, UH, and QH under the entire spectrum of QC; • Adaptability • Evaluate the adaptability of QUTS to changing workloads (QCs); • Sensitivity • Evaluate the sensitivity of QUTS to its two parameters (ω and ). @HKUST, 12/18/2006

  17. QC <qosmax, rtmax, qodmax, uumax> Performance metric Profit Percentage 1. Performance – QC setup 9 sets of QC groups @HKUST, 12/18/2006

  18. Step QCs Linear QCs QUTS gives near maximal profit from both QoS and QoD. Step QCs and linear QCs follow the same trend. 1. Performance – All QC groups @HKUST, 12/18/2006

  19. QUTS consistently achieves near maximal profit under the whole spectrum of QC 1. Performance – Various QC groups FIFO UH QUTS QH @HKUST, 12/18/2006

  20. QUTS adapts to QC changes fast! 2. QUTS Adaptability • Time is divided into four segments. • In odd segments, each QC has • qodmax = 5 x qosmax • In even segments, each QC has • qosmax = 5 x qodmax @HKUST, 12/18/2006

  21. ( = 10ms) Performance varies little for a wide range of ω Betterwhen ≥ transaction execution time 3. QUTS Sensitivity • Adaptation Period ω (ω = 1000ms) • Atom Time @HKUST, 12/18/2006

  22. Outline • Introduction • Quality Contracts • QUTS: Query Update Time Share Scheduling • Experiments • Related work • Conclusions @HKUST, 12/18/2006

  23. Web-databases [Adelberg et al. 1995] [Challenger et al. 2000] [Luo et al. 2002] [Datta et al. 2002] [Labrinidis et al. 2004] [Qu et al. 2006] [Labrinidis et al. 2006] [Xu et al. 2006] … Real time databases [Abbott et al., 1988] [Sha et al., 1991] [Haritsa et al., 1993] [Ramamritham et al., 1994] [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] … Economic Models [Ferguson et al., 1996] [Stonebraker et al., 1996] … Related work @HKUST, 12/18/2006

  24. Conclusions • We proposed • a Quality Contract framework to capture user preferences in web-database systems, and • a meta schedule scheme, QUTS, to dynamically concatenate query queue and update queue according to user queries. • We showed with extensive simulation study on real data traces that • QUTS outperforms the baseline algorithms in the whole spectrum of Quality Contract setup, • QUTS adapts fast to the changing workloads, and • QUTS has little sensitivity to its two parameters. @HKUST, 12/18/2006

  25. Future work • Quality Contract generation strategies • Synthetic workload • Provide suggestions to users to select QCs • I/O scheduling for disk-based Web databases @HKUST, 12/18/2006

  26. Thanks! • Comments & Questions? Huiming Qu huiming@cs.pitt.edu @HKUST, 12/18/2006

  27. Workload characteristics @HKUST, 12/18/2006

  28. Questions • Skewed queries • Atom time -> number of tran. • Why restarts • Network delay comparable to database delay when facing high loads of updates • Scalability point of view • Plot the correlation on time (U vs. Q) @HKUST, 12/18/2006

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