optimizing index allocation for sequential data broadcasting in wireless mobile computing
DESCRIPTION
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. Introduction Preliminaries - PowerPoint PPT PresentationTRANSCRIPT
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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
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Outline
1. Introduction
2. Preliminaries
3. Index Allocation for Skewed Data Access
4. Optimal Order for Sequential Data Broadcasting
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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
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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.
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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
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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.
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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.
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Preliminaries
Tuning timeClient
I
a1 a2 a3
R1 R2 R3 R4 R5 R6 R7 R8 R9
Probe wait
Bcast wait
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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
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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.
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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
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Algorithm CF
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Algorithm CF
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Corresponding data broadcasting sequence
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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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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.
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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
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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( )
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Employing Variant Index Fanouts to Minimize Index Probes
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Employing Variant Index Fanouts to Minimize Index Probes
1 ( )
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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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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
R R R
R j n
R R R
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Algorithm VF
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1 2
1 1
* *
1, 2
*
1 2
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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index node in the index tree.
4.Partition( , , ..., ).
5.Insert into the ordered list ( , , ..., )
and relabel them as ( , , ..., ) according
to descending ord
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er of Pr( ), 1 1.
6. artition( , , ..., ).
7.Re .
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turn
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Algorithm VF
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Algorithm VF
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Algorithm VF
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Algorithm VF
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Algorithm VF
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3
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Experimental Results on Index Allocation
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Experimental Results on Index Allocation
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Optimal Order for Sequence Data Broadcasting
1. Ordering Broadcasting Data to Minimize Data Access Time
2. Experimental Results on Order of Broadcasting
3. Remarks
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Ordering Broadcasting Data to Minimize Data Access Time
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Ordering Broadcasting Data to Minimize Data Access Time
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Algorithm ORD
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Algorithm ORD
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Algorithm ORD
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Experimental Results on Order of Broadcasting
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Remarks
Algorithm Complexity Operation
CF sorting
VF recursive
ORD sorting
)log( nnO
)log( nnO
)log( 2 nnO
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