Wireless Broadcast-based Communications Systems: Overview of Current and Future Research of C.A.C.LAB, A.U.Th.

1. Wireless Broadcast-based Communications Systems:Overview of Current and Future Research ofC.A.C.LAB, A.U.Th. Prof. Georgios Papadimitriou, Liaskos Christos Aristotle University of Thessaloniki Department of Informatics Email: {gp, cliaskos}@csd.auth.gr

2. 2 Contents Ι. Introduction Past Research Objectives - Motivation ΙΙ. Theoretical Background  Push vs Pull, The Broadcast Problem Client Models  Related Work ΙΙΙ. Research of the CACLAB team  Published, Submitted, Completed  Current ΙV. Future Research  New Research Objectives - Justification  Time Schedule

3. Προηγούμενη - Περιεχόμενα- Επόμενη 3 Ι. Introduction

4. Past Research Objectives Προηγούμενη - Περιεχόμενα- Επόμενη Definition of the specifications of the optimal , cost-efficient Wireless Broadcast System #1 #2 Analytical Minimization of the mean client serving timne Cost Minimization (CPU, RAM) U D CONVERGENCE Impact DEPRECATED!! Perf Cost Perform. Cost Perf. Cost

5. Motives Προηγούμενη - Περιεχόμενα- Επόμενη • Inexpensive and Efficient Communications System for: • The continued education of borderland doctors. • The exchange of hospital patients between neighborly states. Honorable Praise, 5thScientific Conference of the Department of Medicine, A.U.Th.  INTERREG IIIA GREECE-BULGARIA , Decision300531/ΥΔ4388 01/11/2005, CrossBorderHealth project  INTERREG IIIA/ARCHIMED grand, C.N. Α.1.087, IntraMed Project

6. Προηγούμενη - Περιεχόμενα- Επόμενη 6 ΙΙ. THEORETICAL BACKGROUND

7. Προηγούμενη - Περιεχόμενα- Επόμενη Push? Pull? Broadcast? OnQuery Server Server Client Client Send Answer ? Usefull Drop • Pros: • Best Performance • Good Channel Utilization • Application Range • Cons: • -Server Load (Scalability)? • -Cost? • -Client –side Hardware? • -Security? (DoS - Impersonation) • Pros: • Minimal Cost • Architecture Simplicity • Lightweight Clients • Absolute Server Security • Cons: • -Non-optimal performance • -Maximum Channel Utilization(++) • -Application Range (Adaptivity?)

8. Προηγούμενη - Περιεχόμενα- Επόμενη The Broadcast Problem • Data item = page • Uniform or not pages • Page <-> Broadcast “Cost” • Define the Broadcast Schedule that minimizes the mean client serving time AND the mean broadcast cost. ++ For Adaptive Push Systems --Minimal Schedule Size --QoS: Guaranteed Serving Time

9. Προηγούμενη - Περιεχόμενα- Επόμενη Client Probabilistic Model Common but not confining!! • Each client accesses a portion (Range) of the available pages. • Pages in Rangeare divided in sets of pages called Region, with common access probability. • TheRegions follow thezipf p.d.f : Each combination of the Range, Regionand θ parameters, defines ap.d.f. and therefore a distinct Client Case

10. Προηγούμενη - Περιεχόμενα- Επόμενη Related Work

11. Προηγούμενη - Περιεχόμενα- Επόμενη Broadcast Disks Defines a scheduling framework, ensuring the periodicity and proportionality characteristics of the final broadcast program. • A feedback mechanism provides a metric of the pages’ access probabilities. • A Clustering Algorithm groups the pages by access probability, into teams called “Disks” • The Virtual Disks rotate around a common axis. • Appropriate rotating speeds are chosen. • Pages are serially extracted • The Broadcast Schedule is produced. The mean client serving time depends on all of the above steps.

12. Προηγούμενη - Περιεχόμενα- Επόμενη -LCM calculation??? -Integer division? -Zero padding extend? BDISKS: Schedule construction algorithm • Calculate a max_chunks parameter as the LCM • of the virtual disks’ speeds • Divide each disks into a number of chunks: • Serialize the chunks: • fori = 0..(max_chunks - 1) • forj = 1..NoD • Broadcast chunk Cji(i mod num_chunks(j)) • end • end

13. Προηγούμενη - Περιεχόμενα- Επόμενη Mathematical Analysis of the no-BC problem • Let L be the number of pages that constitute the Broadcast Sequence. • For each page Pi: • Should Pi be requested by a client, the mean delay time D(Pi) will be: 13

14. Προηγούμενη - Περιεχόμενα- Επόμενη Mathematical Analysis of the no-BC problem Lagrange Parameters Method (CACLAB Extended Analysis)

15. Προηγούμενη - Περιεχόμενα- Επόμενη 15 ΙΙΙ. Research of the CACLAB team

16. Προηγούμενη - Περιεχόμενα- Επόμενη Overview (part 1)

17. Προηγούμενη - Περιεχόμενα- Επόμενη Overview(part 2)

18. Προηγούμενη - Περιεχόμενα- Επόμενη A. TheCWDB scheme • Presentation of a complete, BDISKS based, push system that incorporates: • i. A new feedback mechanism • ii. A K-means based Virtual Disk formation approach • iii. A new Disk speed definition algorithm • Comparison with other related, popular schemes • More than 50 client cases examined for the first time. Clustering-driven Wireless Data Broadcasting (CWDB) GREEDY-based Broadcasting Procedure (GBBP)

19. Προηγούμενη - Περιεχόμενα- Επόμενη A. Feedback Scheme Votes Registry Snapshots 1,000 queries 35,000 queries 10,000 queries

20. Προηγούμενη - Περιεχόμενα- Επόμενη A. Clustering-driven Wireless Data Broadcasting-

21. Προηγούμενη - Περιεχόμενα- Επόμενη A. GREEDY Based Broadcasting Procedure- GBBP

22. Προηγούμενη - Περιεχόμενα- Επόμενη A. CWDB andGBBP Comparison

23. Προηγούμενη - Περιεχόμενα- Επόμενη A. Client Case Definition … compare the best results achieved by each algorithm For each client case … 54 Client cases

24. Προηγούμενη - Περιεχόμενα- Επόμενη A. Results(Ι) Range=1000, Region=30 Range=30, Region=5

25. Προηγούμενη - Περιεχόμενα- Επόμενη A. Results(ΙΙ) Range=2000, Region=100 Range=1000, Region=50

26. Προηγούμενη - Περιεχόμενα- Επόμενη B. Optimization Based Scheduling Don’t split equiprobable pages to different disks 1 Definition of optimal model parameters Broadcast Disks Model Υπόθεση #1 Mathematical Analysis Υπόθεση #2 Projectionofm.c.r.t D Optimal Disk Sizes Optimal Ui Same Delay contribution for all Disks. 2 Validation of Analysis’ Conclusion ► Performance Estimates VsSimulation Results ► Comparison with other heuristic methods 26

27. Προηγούμενη - Περιεχόμενα- Επόμενη B. Mathematical Analysis • Consider N consecutive client queries… • Out of them approximately: • refer to pages belonging to Disk #i, and correspond to an aggregate delay of • and thus the approximate mean delay time will be Positioning of the di is in accordance with assumption #1 27

28. Προηγούμενη - Περιεχόμενα- Επόμενη B. Mathematical Analysis (ΙΙ) • Due to Assumption #2: • Which is expressed in matrix form as: • And usually, thus 28

29. Προηγούμενη - Περιεχόμενα- Επόμενη B. Optimized Broadcast Scheduling Procedure-OBSP • Define Value Sets for Δ and ΝοD  SΔ, SNoD • For each pair (SΔx SNoD ) calculate the approximate mean delay • (CAUTION! L varies!) • Keep the pair (Δoptimal, NoDoptimal) that achieves the minimum • Calculate corresponding di values  Thus [Disk Sizes]optimalis also defined. • Objective #1 Completed • Optimal Parameter values are set (Δoptimal, NoDoptimal, [Disk Sizes]optimal). • An approximation of the mean response time is calculated ( D ) 29

30. Προηγούμενη - Περιεχόμενα- Επόμενη 30 B. Client Cases Definition Run ANALYSISfor parameters’ value sets: For each case …. VALIDATION Keep Optimal NoDo, ΔoDt SIMULATION Dt(NoDo, Δo) Ds(NoDo, Δo) Equal ? Compare ? Keep Optimal NoD, ΔDs For each case …. 54 client cases Run SIMULATIONfor parameters’ value sets: 16

31. Προηγούμενη - Περιεχόμενα- Επόμενη B. Converge of Analysis and Simulation (O.B.P.) Typically, 31

32. Προηγούμενη - Περιεχόμενα- Επόμενη B. Comparison with G.B.P. 32 Equally distributed Workloads Not Working!

33. Προηγούμενη - Περιεχόμενα- Επόμενη B. Conclusion • Analytical means of research on the Broadcast Disks Method were introduced. • A new and very efficient broadcast scheduling scheme was presented, based on workload distribution assumptions. • Was found to be dominant over traditional choices in the vast majority of test cases. • Other Workload distributions need to be tested. 33

34. Προηγούμενη - Περιεχόμενα- Επόμενη C. Extending OBSP beyond uniform Disk Workload Distribution Generalize the DWD distribution  No whole-Region-grouping  No deterministic grouping of zero-voted pages • Non-Deterministic, Page-Grouping or NDPG-Variant. • Non-Deterministic, Region-Grouping or NDRG-Variant • Deterministic, Page-Grouping or DPG-Variant • Deterministic, Region-Grouping or DRG-Variant 34

35. Προηγούμενη - Περιεχόμενα- Επόμενη C. Results Range 500 1500 3000 2000 4500 3500 out of 5000. Region=50

36. Προηγούμενη - Περιεχόμενα- Επόμενη D. AcceleratingUOBP.. requires 4Ν +2 computations.. Thomas Method (better alternative): 8N-4 Thus was proven that the new method requires 50% the computational power in any case.

37. Προηγούμενη - Περιεχόμενα- Επόμενη D. Reducing the Required Memory

38. Προηγούμενη - Περιεχόμενα- Επόμενη CACLAB Research Overview(part 2)

39. Προηγούμενη - Περιεχόμενα- Επόμενη 39 ΙV. Future Research

40. Προηγούμενη - Περιεχόμενα- Επόμενη 40 • #1 • Extension for Huge Databases and umber • Client model extension (drop single equivalent client approach) ΙV. New Research Objectives #2  Optimize feedback mechanisms  Security Issues with respect to adaptivity Current State: Analysis covering serving time, BS Length, QoS, optimization  Cost effective, Data scrambling, adaptivity aware algorithms (cSOA) • #3 • Noise and Error tolerance. • Caching Policies • Client queries correlation • #4 • Hybrid Pull-Push Balancing System

41. Προηγούμενη - Περιεχόμενα- Επόμενη 41 Time Schedule

42. Προηγούμενη - Περιεχόμενα- Επόμενη 42 Why Hybrids?

43. Προηγούμενη - Περιεχόμενα- Επόμενη 43 Questions??