Optimizing Index Allocation for Sequential Data Broadcasting in Wireless Mobile Computing Ming-Syan...

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Optimizing Index Allocation for Sequential Data Broadcasting in Wireless

Mobile Computing

Ming-Syan Chen, Senior Member, IEEE, Kun-Lung Wu, Member, IEEE Computer Society, and Philip S. Yu, Fellow, IEEE

M9129022 郭文漢

Outline

1. Introduction

2. Preliminaries

3. Index Allocation for Skewed Data Access

4. Optimal Order for Sequential Data Broadcasting

Introduction

背景

建立 index tree

Algorithm CF Algorithm VF

Optimal orderfor sequential

data broadcasting

解決方法 效益

節省電力

Algorithm ORD

舊方法問題問題

不使用Data Access Skew

有限電力

Introduction

A mobile client to be able to operate in two different modes: doze mode and active mode.

The structure of an index tree determines the index probing scenario to switch between the doze and the active modes for data access under such an indexed broadcasting.

Data Access Skew : The access frequencies of different data records are usually different from one another.

Introduction

I

a1 a2 a3

R1 R2 R3 R4 R5 R6 R7 R8 R9

I a1 R1 R2 R3 a2 R4 R5 R6 a3 R7 R8 R9

Indexed broadcastingIndex tree

Index probing scenario to data record R5

Preliminaries

A mobile client is assumed to use selective tuning to listen to indexed sequential data broadcasting.

Tuning time : The amount of time spent by a client to listen to the channel.

Access time : The time elapsed from the time a client wants an identified record to the time that record is downloaded by the client.

Preliminaries

Probe wait : The time from the point a client tunes in to the point when the first index is reached.

Bcast wait : Time duration from the point the first index is reached to the point the required record is obtained.

Preliminaries

Tuning timeClient

I

a1 a2 a3

R1 R2 R3 R4 R5 R6 R7 R8 R9

Probe wait

Bcast wait

Index Allocation For Skewed Data Access

1. Imbalanced Index Tree Construction for Fixed Fanouts

2. Employing Variant Index Fanouts to Minimize Index Probes

3. Experimental Results on Index Allocation

Imbalanced Index Tree Construction for Fixed Fanouts

Algorithm CF will reduce the number of index probes for hot data while allowing more probes for cold data.

Algorithm CF : Use access frequencies to build an index tree with a fixed fanout d.

Algorithm CF (bottom up manner)

Step 1 : Every single node labeled with the corresponding access frequency.

Step 2 : Attach the d subtrees with the smallest labels to a new node. Label the resulting subtree with the sum of all labels from its d child subtrees.

Step 3 : n=n-d+1. If n=1 stop else goto Step2

Algorithm CF

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Algorithm CF

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Corresponding data broadcasting sequence

Cost Model

Theorem 1: Given a fixed index fanouts, the average

number of index probes is minimized by using the index tree constructed by algorithm CF.

Cost model

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Cost Model

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Employing Variant Index Fanouts to Minimize Index Probes

An efficient heuristic algorithm VF to build an index tree with variant fanouts.

We want data records to stay as close to the root as possible.

Algorithm VF strikes a compromise between these conflicting factors( larger fanouts) and minimizes the average cost of index probes.

Employing Variant Index Fanouts to Minimize Index Probes

1 2

Lemma 1.

Suppose that node r has m child nodes, , , ..., ,

which are sorted according to descending order of Pr( ),

1 , i.e. Pr( ) Pr( ) if and only if j .Then, the

average cost of index

m

j

j k

h h and h

h

j m h h k

1 2

1 1

probes can be reduced by grouping

nodes , , ..., and and attaching them under a new

child node if and only if

( 1) Pr( ) Pr( )

i i m

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h h h

m i h h

Employing Variant Index Fanouts to Minimize Index Probes

( )

1 ( )

1 1 ( )

Pr( )( ( ) )

Pr( )( ( ) ) Pr( )( ( ) )

p j

p PT h p jj

BE j pj m a PT h

j p j pj i a i j m a PT h

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Employing Variant Index Fanouts to Minimize Index Probes

1 ( )

1 ( )

Pr( )( ( ) ( 1))

Pr( )( ( ) ( 1 ))

p j

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AF j pj i a PT h

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Employing Variant Index Fanouts to Minimize Index Probes

1 1

1 1

( 1 ) Pr( ) Pr( )

( 1 ) Pr( ) Pr( )

AF BE j jj i i j m

j jj i i j m

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i m h h

Algorithm VF (top down manner)

1 2

1 2

Algorithm VF:

Step 1:Assume that , , ..., and have been sorted

according to descending order of Pr( ), 1 .

Step 2:Partition( , , ..., ).

Step 3:Report the resulting index tree.

n

j

n

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Algorithm VF

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Procedure Partition( , , ..., ):

1.Let ( ) ( 1) Pr( ) Pr( ).

Determine such that ( ) max ( )

2.If y(i ) 0, then return.

3.Attach nodes , , ..., under a

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4.Partition( , , ..., ).

5.Insert into the ordered list ( , , ..., )

and relabel them as ( , , ..., ) according

to descending ord

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6. artition( , , ..., ).

7.Re .

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Algorithm VF

Algorithm VF

Algorithm VF

Algorithm VF

Algorithm VF

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Experimental Results on Index Allocation

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Experimental Results on Index Allocation

Optimal Order for Sequence Data Broadcasting

1. Ordering Broadcasting Data to Minimize Data Access Time

2. Experimental Results on Order of Broadcasting

3. Remarks

Ordering Broadcasting Data to Minimize Data Access Time

Ordering Broadcasting Data to Minimize Data Access Time

Algorithm ORD

Algorithm ORD

Algorithm ORD

Experimental Results on Order of Broadcasting

Remarks

Algorithm Complexity Operation

CF sorting

VF recursive

ORD sorting

)log( nnO

)log( nnO

)log( 2 nnO

謝謝

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