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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

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)
  • 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)

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
  • 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
  • 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

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?
  • 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)

ADMT Lab, Department of Computer Science, University of Pittsburgh

outline1
Outline
  • 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

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

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

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

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

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
  • 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
  • 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

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

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

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

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(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.

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

[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
  • Motivating Example
  • Performance metric: User Satisfaction
  • System overview & algorithms
  • Experiments
  • Related work
  • Conclusions

ADMT Lab, Department of Computer Science, University of Pittsburgh

conclusions
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.

ADMT Lab, Department of Computer Science, University of Pittsburgh

thank you

Thank you!

Huiming Qu

[email protected]

Questions and Comments

user requirements1
User Requirements
  • Timeliness: Meeting deadlines
  • Freshness: Meeting freshness requirements

ADMT Lab, Department of Computer Science, University of Pittsburgh

performance metrics
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.

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
  • 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
  • UNIT outperforms others in all cases.

ADMT Lab, Department of Computer Science, University of Pittsburgh

ad