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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. QUERIES. UPDATES. Stock Trading Services (ideal).

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Unit user centric transaction management in web database systems

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


Stock trading services ideal

QUERIES Systems

UPDATES

Stock Trading Services (ideal)

Web databases

GOOG

$367.9

GOOG

IBM

IBM

$75.8

ADMT Lab, Department of Computer Science, University of Pittsburgh


Stock trading services reality
Stock Trading Services (reality) Systems

  • To avoid overloading:

  • increase hardware capacity, or

  • adding software support

Web databases

GOOG

OVERLOADED!

GOOG

GOOG

GOOG

GOOG

GOOG

OTE

IBM

GOOG

SUN

IBM

GOOG

GOOG

GOOG

OTE

MSFT

ADMT Lab, Department of Computer Science, University of Pittsburgh


Stock trading services unit
Stock Trading Services (UNIT) Systems

UNIT

MSFT

Web databases

GOOG

GOOG

GOOG

$367.9

OTE

IBM

SUN

IBM

TUTU

$75.8

OTE

ADMT Lab, Department of Computer Science, University of Pittsburgh


Problem statement
Problem Statement Systems

  • Users’ satisfaction are based on:

    • Freshness: query is answeredbased on fresh data

    • Timeliness: query is answeredwith 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.

  • ADMT Lab, Department of Computer Science, University of Pittsburgh


    Outline
    Outline Systems

    • Motivating Example

    • Performance metric: User Satisfaction

    • System overview & algorithms

    • Experiments

    • Related work

    • Conclusions

    ADMT Lab, Department of Computer Science, University of Pittsburgh


    User requirements

    Q1 returns with U1 Systems

    U1

    U3

    U2

    t

    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 <deadline, freshness>

    ADMT Lab, Department of Computer Science, University of Pittsburgh


    Is success ratio enough
    Is Success Ratio Enough? Systems

    • 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.

    ADMT Lab, Department of Computer Science, University of Pittsburgh


    User satisfaction metric usm
    User Satisfaction Metric (USM) Systems

    ADMT Lab, Department of Computer Science, University of Pittsburgh


    Outline1
    Outline Systems

    • Motivating Example

    • Performance metric: User Satisfaction

    • System overview & algorithms

    • Experiments

    • Related work

    • Conclusions

    ADMT Lab, Department of Computer Science, University of Pittsburgh


    Unit system user centric trans action management

    Reject Systems

    Failure

    Deadline

    Missed

    Failure

    Data

    Stale

    Failure

    Success

    UNIT System (User-ceNtrIc Trans-action Management)

    UNIT

    Updates

    Queries

    Admission

    Control

    Frequency

    Modulation

    • 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

    Data

    +/- updates

    +/- queries

    Statistics

    USM Load Balancing Controller

    ADMT Lab, Department of Computer Science, University of Pittsburgh


    Load balancing controller
    Load Balancing Controller Systems

    Success Gain

    +

    Increase # of queries

    Gain

    Gain

    0

    Rejection

    Cost

    Rejection

    Cost

    Increase # of updates

    Data Stale

    Cost

    Data Stale

    Cost

    Deadline Missed

    Cost

    Deadline Missed

    Cost

    -

    Decrease # of updates

    Decrease # of queries

    Failure Cost

    ADMT Lab, Department of Computer Science, University of Pittsburgh


    Query admission control

    q6 Systems

    q6

    q7

    q7

    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?

    Current time

    q4

    deadline

    q5-7

    deadlines

    q4

    q1

    q2

    q3

    q5

    time

    ADMT Lab, Department of Computer Science, University of Pittsburgh


    Query admission control cont

    q1 Systems

    q2

    q3

    q4

    q5

    q1

    q2

    q3

    q1

    q2

    q3

    q6

    q7

    Query Admission Control (cont.)

    • Use Cflex to Increase/Decrease # of queries

      • Decrease Cflex to increase queries admitted

      • Increase Cflex to decrease queries admitted

    q4

    deadline

    q5-7

    deadlines

    Current time

    smaller Cflex

    larger Cflex

    time

    Cflex

    ADMT Lab, Department of Computer Science, University of Pittsburgh


    Update frequency modulation

    Decrease # of Updates Systems

    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.

    U1

    U1

    U1

    U1

    Q3

    Update Frequency Modulation

    D1 is picked to reduce its updates!

    D3

    D1

    D2

    ADMT Lab, Department of Computer Science, University of Pittsburgh


    Outline2
    Outline Systems

    • Motivating Example

    • Performance Metric: User Satisfaction

    • System Overview & Algorithms

    • Experiments

    • Related Work

    • Conclusions

    ADMT Lab, Department of Computer Science, University of Pittsburgh


    Algorithms compared
    Algorithms Compared Systems

    • IMU

      • Immediate Update, no admission control, 100% freshness

    • ODU

      • On-demand Update, no admission control, 100% freshness

    • QMF: [Kang,TKDE’04]

      • Immediate update, admission control, no weights among rejection, timeliness and freshness requirements are considered.

    • UNIT

      • is what U need 

    ADMT Lab, Department of Computer Science, University of Pittsburgh


    Experiment design
    Experiment Design Systems

    We want to evaluate the following:

    • Effectiveness of the update frequency modulation,

    • Performance under the naïve USM setting (= Success Ratio),

    • Performance under various USM settings,

    • Distribution of four query outcomes under various USM settings.

    ADMT Lab, Department of Computer Science, University of Pittsburgh


    Experimental setup

    Query trace Systems

    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

    Experimental Setup

    ADMT Lab, Department of Computer Science, University of Pittsburgh


    1 update frequency modulation evaluation

    few queries Systems

    Updates can be removed without hurting query freshness.

    1. Update Frequency Modulation Evaluation

    Query Distributions on Data

    Update Distributions on Data

    (med-unif)

    ADMT Lab, Department of Computer Science, University of Pittsburgh


    1 update frequency modulation evaluation cont

    few queries Systems

    A very small portion of updates are needed to keep queries freshness high.

    1. Update Frequency Modulation Evaluation (cont.)

    Query Distributions on Data

    Update Distributions on Data

    (med-neg)

    ADMT Lab, Department of Computer Science, University of Pittsburgh


    2 na ve usm success ratio gain 1 penalties 0
    2. Naïve USM = Success Ratio Systems(gain = 1, penalties = 0)

    positive correlation

    negative correlation

    • UNIT has the least performance drop when workload increases.

    ADMT Lab, Department of Computer Science, University of Pittsburgh


    3 usm gain 1 penalties 0

    UNIT has the least penalties Systems.

    UNIT has the highest gain.

    3. USM (gain = 1, penalties ≠ 0)

    Case 1 - Gain dominates:

    penalties = 0.1 or 0.5

    Case 2 - Penalty dominates:

    penalties = 1 or 5

    ADMT Lab, Department of Computer Science, University of Pittsburgh


    4 query outcome distributions

    UNIT obtains higher success ratio than others because it keeps queries from falling into the categories that have higher penalties.

    4. Query outcome distributions

    • 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

    ADMT Lab, Department of Computer Science, University of Pittsburgh


    Related work

    Web-databases keeps queries from falling into the categories that have higher penalties.

    [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]

    Related work

    ADMT Lab, Department of Computer Science, University of Pittsburgh


    Outline3
    Outline keeps queries from falling into the categories that have higher penalties.

    • Motivating Example

    • Performance metric: User Satisfaction

    • System overview & algorithms

    • Experiments

    • Related work

    • Conclusions

    ADMT Lab, Department of Computer Science, University of Pittsburgh


    Conclusions
    Conclusions keeps queries from falling into the categories that have higher penalties.

    • 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.

    ADMT Lab, Department of Computer Science, University of Pittsburgh


    Thank you

    Thank you! keeps queries from falling into the categories that have higher penalties.

    Huiming Qu

    [email protected]

    Questions and Comments





    User requirements1
    User Requirements Pittsburgh

    • Timeliness: Meeting deadlines

    • Freshness: Meeting freshness requirements

    ADMT Lab, Department of Computer Science, University of Pittsburgh


    Performance metrics
    Performance Metrics Pittsburgh

    • 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.

    Q1 returns with U1:$300

    U1:$300

    U2:$310

    U3:$350

    t

    ADMT Lab, Department of Computer Science, University of Pittsburgh


    Update frequency modulation1
    Update Frequency Modulation Pittsburgh

    • Degrade Update

      • Each data item maintains a Degrading Ticket Value Tj

      • Lottery Schemes [Waldspurger 95], higher ticket value means more probably to be degraded.

      • Query decrease Tj by DTj, Update increase Tj by ITj

      • If picked, it is degraded by 10%.

    • Upgrade Update

      • randomly pick a degraded data item

      • Upgrade it by 50%

    ADMT Lab, Department of Computer Science, University of Pittsburgh


    Na ve usm
    Naïve USM Pittsburgh

    • UNIT outperforms others in all cases.

    ADMT Lab, Department of Computer Science, University of Pittsburgh


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