Spring 2002 1

Efficient Dissemination of Enterprise Summary Data to Mobile Clients

Mohamed A. SharafUniversity of Pittsburgh

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Motivation

“…Currently handheld and palmtop computers are widely used for personal information management. In the near future they will also be used to access enterprise data…”, IBM Corp., 2000

“…For organizations to be successful in today's fast-paced digital economy, decision makers require access to all business-critical information on any platform. Wireless devices are quickly becoming alternative platforms for e-enabling the enterprise, as they provide instant access to relevant enterprise data for Mobile Decision Making…”, Hummingbird Communications Ltd., 2000

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Outline

OLAP (On-Line Analytical Processing) Data ModelWireless OLAP ModelScheduling AlgorithmsSimulation ResultsConclusion

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Multi-Dimensional Model [Codd93]Pro

duct

TV

VCRPC

Date 1Qtr 2Qtr 3Qtr 4Qtr

Cou

ntr

y

U.S.A

Canada

Mexico

Group-By (P,C,D),

Sum(Sales)

Dimensions

Measures

5

A Sample Data CubePro

duct

TV

VCRPC

Date 1Qtr 2Qtr 3Qtr 4Qtr

Cou

ntr

y

U.S.A

Canada

Mexico

G(P,C)

G(P)

Derivation Dependency

6

Traditional OLAP Server

Point to Point Access

7

Wireless OLAP Server

Broadcast

Uplink Channel

Power Consumption

8

Wireless Environments

Asymmetry in the communicationBroadcast for data dissemination Periodic (push-based) On-Demand (pull-based) Hybrid

A broadcast schedule determines what and when to broadcast Metrics Access Time = Wait + Tune Power Consumption = Active + Doze

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On-Demand Scheduling Algorithms

First-Come First-Serve (FCFS)Shortest Service Time First (SSTF)

RxW: broadcast a page either because it is popular or because it has at least one long-outstanding request [Franklin 99]

Most Request First (MRF) [Ammar 86]

Summary Tables : 1) Heterogeneous2) Skewed Access3) Derivation Dependency

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Broadcast Organization

Header Packet = Identifier + PointerA table TX is characterized by set of dimensional attributes X . TX subsumes TY, iff Y X, similarly, TY is dependent on TX

X is the dimensionality degree

100 G(Supp) 111 G(Supp, Prod, Cust) … Tune Wait Tune Tune

Target TableHeader Table

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RxW Variants

Strict RxW/S: For each request QX for a summary table TX, the

server maintains the following values: R: The number of requests for TX. W: The age of the first request has for table

TX. S: The size of table TX.

Table with highest RxW/S is the one to broadcast.

Flexible RxW/S: Decision is same as RxW/S, but using “Derivation

Dependency” allows: Server to remove dependent tables from queue Client to tune to the first subsuming table

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Controlling the Flexibility

Why ? Compromise between access time and

power consumption

How ? Integrate derivation dependency with

scheduling decision Classify dependents into beneficial and

impairing according to dimensionality

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d3d1 d2 d4

d1,d2 d1,d3

d1,d2,d3 d1,d2,d4 Benefit(B)

Impairment(I)

d3d1 d2 d4

d1,d2 d1,d3

d1,d2,d3 d1,d2,d4 Benefit(B)

Scheduling Intuition

d1,d2,d3,d4,d5

d5

d4,d5

d3d1 d2 d4

d1,d2 d1,d3

d1,d2,d3 d1,d2,d4 d3,d4,d5

d1,d2,d3,d4 d2,d3,d4,d5d1,d2,d3,d5

QX

distance = X /2

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Benefit/Impairment Scheduling (BI)

• The BI for Qi is computed as:

Bj Ikkiji

Bj Ikkji

Bj Ikkji

SSSS

WWWRRR

)(

)()(

• The highest BI value request is broadcast next and dependents B are removed

• A priority queue is used to store requests

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Experiments• A synthesized six-dimension lattice.• Packet capacity = 10 attribute values• Each Mobile host poses 100 queries

according to a Zipf distribution• Each experiment was run 5 times• Metrics:

• Average Access Time in simulation ticks• Average Power consumption in doze units

• Active power = 20 times doze power• Fairness: Standard Deviation of requests’

stretch [Acharya 98]• stretch = access time/service time

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Average Access Time

Number of Clients

0 50 100 150 200

Acc

ess

Tim

e (

Sim

ula

tion

Pa

cke

ts)

0

5000

10000

15000

20000

25000

SSTFFCFSRxWRxW/SFlex. RxW/SBI

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Power Consumption

Number of Clients

0 100 200 300

Pow

er

Con

sum

ptio

n (D

oze

Uni

ts)

6000

8000

10000

12000

14000

16000

18000

RxWRxW/SFlex. RxW/SBI

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Fairness

Number of Clients

0 50 100 150 200

Fa

irn

ess

Me

tric

1

10

100

1000

10000

SSTFFCFSRxWRxW/SFlex. RxW/SBI

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Varying Skewness

Zipf Parameter (

0.0 0.2 0.4 0.6 0.8 1.0

Acce

ss T

ime

(S

imu

latio

n P

acke

ts)

0

5000

10000

15000

20000

25000

30000

35000

SSTFFCFSRxWRxW/SFlex. RxW/SBI

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Conclusion

We introduced the new problem of scheduling objects with a derivation dependency propertyWe proposed a variety of scheduling algorithms that minimize access time and preserve power consumption

Load AAT PC

Low BI RxW/S

Med BI RxW/S

High Flex. RxW/S BI

65% less than RxW & 55%

less than RxW/S70% less than RxW & 55%

less than RxW/S77% less than RxW

15% less than RxW20% less than RxW

24% less than RxW

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Future Work

We are planning to extend the research to include: Subscribe push environment Caching mechanisms More detailed cost model