1. SOM: Dynamic Push-Pull Channel Allocation Framework For Mobile Data Broadcasting
Jiun-Long Huang, Wen-Chih Peng, and Ming-Syan Chen
IEEE Transactions on Mobile Computing, Vol.5, No.8, Aug. 2006
Presented by Jing David Dai
Dept. CS, VT

2. Outline Introduction State-of-art and Problem Formulation
Analytical Models
SOM (Solution Mapping)
Performance Evaluation
Conclusion
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3. Introduction Increasing popular mobile computing environments
Stock activities, traffic reports, weather forecasts, …
Wireless mobile clients
Small batteries, limited bandwidth
Design issue
Conserve the energy and communication bandwidth of a mobile unit while allowing mobile users of the ability to access information from anywhere at anytime
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4. Introduction Data delivery modes Broadcast (push) On-demand (pull)
Dynamic data and channel allocation (hybrid)
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5. Introduction Dynamic data and channel allocation
Change data delivery configuration to achieve optimal performance
If the load is heavy, more broadcast
If the load is light, more on-demand
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Lighter Load  Heavier Load

6. Introduction Contributions of this paper
Describes the analysis model of dynamic allocation approach
Proposes algorithm SOM to find optimal allocation
Devises algorithm BIS to dynamically partition data items and channels
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7. State-of-art and Problem Formulation
Currently not many multi-channel push and multi-channel pull approach
One broadcast channel, one on-demand channel, fixed or dynamic data cut
One broadcast channel, multiple on-demand channels
One approach with multi-channel push and pull uses flat broadcast program
Not efficient for data with different access probability
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8. State-of-art and Problem Formulation
System description
n: # of data items (n=no+nb)
no: # of data items in on-demand channels
nb: # of data items in broadcast channels
Ri: the ith data item (0<=i<=n)
K: # of channels (K=Ko+Kb)
Ko: # of on-demand channels
Kb: # of broadcast channels
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9. State-of-art and Problem Formulation
Data dissemination
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10. State-of-art and Problem Formulation
Tasks to dynamically allocate data and channels
Determine Ko and Kb
Determine no and nb
Construct hierarchical broadcast program
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11. Analytical Models Broadcast channels
Wb(Kb, nb): minimal average access time for data in broadcast channels
C(K1, n1): the configuration that Kb=K1 and nb=n1
Methods to partition data to broadcast channels
OPT: can find optimal solution but time-consuming
VFK: efficiently gets the close-optimal solution
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12. Analytical Models On-demand channels
Wo(Ko, no): minimal average access time for data in on-demand channels
Pno (no): probability that the requested data item is in on-demand channels as one of the no items
: request arrival rate
o= Pno (no) * : request arrival rate for on-demand channels
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13. Analytical Models On-demand channels (Cont.)
On-demand channels = M/M/c queuing system with arrival rate o
Service rateμ= bandwidth/(data_size+requests)
Based on queuing theory, Wo(Ko, no)=
where
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14. Analytical Models Overall average access time
Probability of a requested data item in on-demand channel
Average access time
Minimal average access time
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15. Analytical Models Trade-off of dynamic data dissemination 2/21/2020
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16. SOM (Solution Mapping)
Problem transformation
Find the best allocation for data items and channels = find C(Kb, nb) with minimal W(Kb, nb), where 0<=Kb<=K and 0<= nb<=n
Search space = (K+1)*(N+1)
SOM process
Search space pruning phase
Solution searching phase
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17. SOM (Solution Mapping)
Search space pruning based on following requirements
nb >= Kb when 0< Kb <K
nb < n when Kb < K
nb = 0 when Kb = 0
no = 0 when Ko = 0
< 1
Prune effects
using property 1-4
Lower bound:
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18. SOM (Solution Mapping)
Solution searching
LocalOptimalCheck: test a configuration is local optimal or not, if not, return the direction to local optimal
LocalOptimalPrediction: predict the position of local optimal based on extrapolation
BIS process: iteratively tests the unpruned configurations; uses above two functions to find local optimal for all Kb; finally returns the best solution.
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19. SOM (Solution Mapping)
BIS-Incremental
Integrate BIS with VFK
Since VFK is a greedy approach to find optimal cut point of data items, the intermediate results in VFK can be reused in BIS
Each time when BIS trying to calculate Wb, it first check whether it has been calculated by VFK
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20. SOM (Solution Mapping)
Complexity analysis
BIS: O(K log n) * time complexity of broadcast program
BIS-Incremental: O(K log n) * 1/k * Complexity of VFK = O(log n) * K * (O(K log K)+O(n))
Space complexity: O(K*n)
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21. Performance Evaluation
Simulation Model
Access frequency:
: parameter of Zipf distribution
 = 0: uniform distribution
Large  : skewed distribution
Five schemes for comparison
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22. Performance Evaluation
Skewness of access frequency
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23. Performance Evaluation
Number of data items
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24. Performance Evaluation
Number of channels
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25. Performance Evaluation
Number of clients
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26. Conclusion
This paper describes the analysis model of multiple broadcast and on-demand channels.
Algorithm SOM, including solution searching approach BIS, is proposed to find optimal allocations of data items and channels.
Simulations results show the efficiency and scalability of SOM.
The authors didn’t address how to re-allocate the data items and channels.
The quality of the optimal solution from SOM is not evaluated.
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27. Thank you!