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Jiun-Long Huang, Wen-Chih Peng, and Ming-Syan Chen

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Presentation on theme: "Jiun-Long Huang, Wen-Chih Peng, and Ming-Syan Chen"— Presentation transcript:

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 2/21/2020 CS 6204

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 2/21/2020 CS 6204

4 Introduction Data delivery modes Broadcast (push) On-demand (pull)
Dynamic data and channel allocation (hybrid) 2/21/2020 CS 6204

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 2/21/2020 CS 6204 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 2/21/2020 CS 6204

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 2/21/2020 CS 6204

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 2/21/2020 CS 6204

9 State-of-art and Problem Formulation
Data dissemination 2/21/2020 CS 6204

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 2/21/2020 CS 6204

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 2/21/2020 CS 6204

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 2/21/2020 CS 6204

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 2/21/2020 CS 6204

14 Analytical Models Overall average access time
Probability of a requested data item in on-demand channel Average access time Minimal average access time 2/21/2020 CS 6204

15 Analytical Models Trade-off of dynamic data dissemination 2/21/2020
CS 6204

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 2/21/2020 CS 6204

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: 2/21/2020 CS 6204

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. 2/21/2020 CS 6204

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 2/21/2020 CS 6204

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) 2/21/2020 CS 6204

21 Performance Evaluation
Simulation Model Access frequency: : parameter of Zipf distribution  = 0: uniform distribution Large  : skewed distribution Five schemes for comparison 2/21/2020 CS 6204

22 Performance Evaluation
Skewness of access frequency 2/21/2020 CS 6204

23 Performance Evaluation
Number of data items 2/21/2020 CS 6204

24 Performance Evaluation
Number of channels 2/21/2020 CS 6204

25 Performance Evaluation
Number of clients 2/21/2020 CS 6204

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. 2/21/2020 CS 6204

27 Thank you!


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