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

Παρουσίαση με θέμα: "Wireless Broadcast-based Communications Systems: Overview of Current and Future Research of C.A.C.LAB, A.U.Th. Prof. Georgios Papadimitriou, Liaskos Christos."— Μεταγράφημα παρουσίασης:

1 Wireless Broadcast-based Communications Systems: Overview of Current and Future Research of C.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 3 Ι. Introduction ΠροηγούμενηΠροηγούμενη - Περιεχόμενα - ΕπόμενηΠεριεχόμενα Επόμενη

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

5 5 Motives  Inexpensive and Efficient Communications System for:  The continued education of borderland doctors.  The exchange of hospital patients between neighborly states.  INTERREG IIIA GREECE- BULGARIA, Decision 300531/ΥΔ4388 01/11/2005, CrossBorderHealth project  INTERREG IIIA/ARCHIMED grand, C.N. Α.1.087, IntraMed Project Honorable Praise, 5 th Scientific Conference of the Department of Medicine, A.U.Th. Honorable Praise, 5 th Scientific Conference of the Department of Medicine, A.U.Th. ΠροηγούμενηΠροηγούμενη - Περιεχόμενα - ΕπόμενηΠεριεχόμενα Επόμενη

6 6 ΙΙ. THEORETICAL BACKGROUND ΠροηγούμενηΠροηγούμενη - Περιεχόμενα - ΕπόμενηΠεριεχόμενα Επόμενη

7 7 Push? Pull? Broadcast? ΠροηγούμενηΠροηγούμενη - Περιεχόμενα - ΕπόμενηΠεριεχόμενα Επόμενη Server Client OnQuery Send Answer Server Client ? 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 8 The Broadcast Problem ΠροηγούμενηΠροηγούμενη - Περιεχόμενα - ΕπόμενηΠεριεχόμενα Επόμενη - 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  Each client accesses a portion (Range) of the available pages.  Pages in Range are divided in sets of pages called Region, with common access probability.  The Regions follow the zipf p.d.f : Each combination of the Range, Region and θ parameters, defines a p.d.f. and therefore a distinct Client Case Each combination of the Range, Region and θ parameters, defines a p.d.f. and therefore a distinct Client Case 9 Client Probabilistic Model ΠροηγούμενηΠροηγούμενη - Περιεχόμενα - ΕπόμενηΠεριεχόμενα Επόμενη Common but not confining!!

10 10 Related Work ΠροηγούμενηΠροηγούμενη - Περιεχόμενα - ΕπόμενηΠεριεχόμενα Επόμενη

11 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. 11 Broadcast Disks ΠροηγούμενηΠροηγούμενη - Περιεχόμενα - ΕπόμενηΠεριεχόμενα Επόμενη

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

13 13 ΠροηγούμενηΠροηγούμενη - Περιεχόμενα - ΕπόμενηΠεριεχόμενα Επόμενη Mathematical Analysis of the no-BC problem

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

15 15 ΙΙΙ. Research of the CACLAB team ΠροηγούμενηΠροηγούμενη - Περιεχόμενα - ΕπόμενηΠεριεχόμενα Επόμενη

16 TitlePublicationShort Description «Clustering-driven Wireless Data Broadcasting»  SCVT 2008  WCM, to appear.Clustering+BD method.New feedback scheme.Complete procedure (CWDB).Checked 54 client cases «An Analytical Approach to the Design of Wireless Broadcasts Disks Systems»  ISADS 2009.Introducing Disk Workload Distribution (DWD) concept.Mathematical Analysis (under DWD+ assumptions).Introducing Optimization Based Procedure (OBP).Checked 1 DWD, 54 client cases «On the Analytical Performance Optimization of Wireless Data Broadcasting»  TVT, to appear.General BD Mathematical Analysis (DWD assumptions only) & Updated Optimization Based Procedure (UOBP).Checked 30 DWD *90 c lient cases «A New Approach to the Design of Wireless Data Broadcasting Systems: An Analysis- Based Cost-Effective Scheme»  ICUMT 2009  BTS, submitted.Faster UOBP convergence algorithm.Memory Efficient Broadcast Sequence Constructor Algorithm Implementation 16 Overview (part 1) ΠροηγούμενηΠροηγούμενη - Περιεχόμενα - ΕπόμενηΠεριεχόμενα Επόμενη

17 TitlePublicationShort Description «Towards Realizable Wireless Broadcast Systems»  TNET, submitted  MELECON2010, submited.Extended, client p.d.f agnostic, BS length aware mathematical analysis of no-bc broadcast problem.Introduction of cost-efficient scheduling algorithm.Deprecation of BDISKS model and State-of-art scheduling algorithms with respect to realizability. «Maximizing Adaptivity of dynamic Wireless Broadcast Systems»  Completed.Analysis on BS length minimization, which achieves minimal serving times. Analysis on the connection between adaptivity and BS length..Advanced Cost efficient, Adaptivity oriented algorithm «QoS on wireless broadcast Systems: Guaranteeing Serving time for real-time applications»  Completed.Analysis on BS construction aimed at guaranteeing serving time with X% certainty..Impact on the client set. 17 Overview (part 2) ΠροηγούμενηΠροηγούμενη - Περιεχόμενα - ΕπόμενηΠεριεχόμενα Επόμενη

18  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. 18 A. The CWDB scheme ΠροηγούμενηΠροηγούμενη - Περιεχόμενα - ΕπόμενηΠεριεχόμενα Επόμενη

19 19 A. Feedback Scheme ΠροηγούμενηΠροηγούμενη - Περιεχόμενα - ΕπόμενηΠεριεχόμενα Επόμενη

20 20 A. Clustering-driven Wireless Data Broadcasting- ΠροηγούμενηΠροηγούμενη - Περιεχόμενα - ΕπόμενηΠεριεχόμενα Επόμενη

21 21 A. GREEDY Based Broadcasting Procedure- GBBP ΠροηγούμενηΠροηγούμενη - Περιεχόμενα - ΕπόμενηΠεριεχόμενα Επόμενη

22 22 A. CWDB and GBBP Comparison ΠροηγούμενηΠροηγούμενη - Περιεχόμενα - ΕπόμενηΠεριεχόμενα Επόμενη

23 23 Range/Region 30/1 30/3 30/5 1000/30 1000/50 2000/50 2000/100 3000/50 4000/50 Θ 0.3 0.5 0.7 0.95 1.1 1.5 54 Client cases 54 Client cases For each client case … CWDB NoD2 3 4 5 6 7 Δ1 2 3 4 5 6 7 8 15 20 50 80 100 U N-1 10 30 50 100 GBBP NoD2 3 4 5 6 7 Δ1 2 3 4 5 6 7 8 15 20 50 80 100 … compare the best results achieved by each algorithm A. Client Case Definition ΠροηγούμενηΠροηγούμενη - Περιεχόμενα - ΕπόμενηΠεριεχόμενα Επόμενη

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

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

26 26 Definition of optimal model parameters Broadcast Disks Model Mathematical Analysis Optimal Disk Sizes Optimal Disk Sizes Optimal U i Optimal U i Projectiono f m.c.r.t D Validation of Analysis’ Conclusion B. Optimization Based Scheduling Υπόθεση #1 Υπόθεση #2 ΠροηγούμενηΠροηγούμενη - Περιεχόμενα - ΕπόμενηΠεριεχόμενα Επόμενη

27 27  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 d i is in accordance with assumption #1 B. Mathematical Analysis ΠροηγούμενηΠροηγούμενη - Περιεχόμενα - ΕπόμενηΠεριεχόμενα Επόμενη

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

29 29  Define Value Sets for Δ and ΝοD  S Δ, S NoD  For each pair (S Δ x S NoD ) calculate the approximate mean delay (CAUTION! L varies!)  Keep the pair (Δ optimal, NoD optimal ) that achieves the minimum  Calculate corresponding d i values  Thus [Disk Sizes] optimal is also defined.  Objective #1 Completed  Optimal Parameter values are set (Δ optimal, NoD optimal, [Disk Sizes] optimal ).  An approximation of the mean response time is calculated ( D ) B. Optimized Broadcast Scheduling Procedure-OBSP ΠροηγούμενηΠροηγούμενη - Περιεχόμενα - ΕπόμενηΠεριεχόμενα Επόμενη

30 30 Range/Region 30/1 30/3 30/5 1000/30 1000/50 2000/50 2000/100 3000/50 4000/50 Θ 0.3 0.5 0.7 0.95 1.1 1.5 54 client cases 54 client cases Optimization Based Procedure S NoD [2 – 15] SΔSΔ [1 – 150] GREEDY [2] Based Procedure S NoD 2 3 4 5 6 7 SΔSΔ 1 2 3 4 5 6 7 8 15 20 50 80 100 For each case …. Run ANALYSIS for parameters’ value sets: Run ANALYSIS for parameters’ value sets: For each case …. Run SIMULATION for parameters’ value sets: Run SIMULATION for parameters’ value sets: Keep Optimal NoD o, Δ o  D t Keep Optimal NoD, Δ  D s 16 SIMULATION D t (NoD o, Δ o ) D s (NoD o, Δ o ) SIMULATION D t (NoD o, Δ o ) D s (NoD o, Δ o ) Equal ? VALIDATION Compare B. Client Cases Definition ΠροηγούμενηΠροηγούμενη - Περιεχόμενα - ΕπόμενηΠεριεχόμενα Επόμενη

31 31 Typically, ΠροηγούμενηΠροηγούμενη - Περιεχόμενα - ΕπόμενηΠεριεχόμενα Επόμενη

32 32 Equally distributed Workloads Not Working! ΠροηγούμενηΠροηγούμενη - Περιεχόμενα - ΕπόμενηΠεριεχόμενα Επόμενη

33 33  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.  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. ΠροηγούμενηΠροηγούμενη - Περιεχόμενα - ΕπόμενηΠεριεχόμενα Επόμενη

34 34  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 ΠροηγούμενηΠροηγούμενη - Περιεχόμενα - ΕπόμενηΠεριεχόμενα Επόμενη

35 Range 3000 5001500 2000 3500 4500 out of 5000. Region=50 ΠροηγούμενηΠροηγούμενη - Περιεχόμενα - ΕπόμενηΠεριεχόμενα Επόμενη

36 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. ΠροηγούμενηΠροηγούμενη - Περιεχόμενα - ΕπόμενηΠεριεχόμενα Επόμενη

37 ΠροηγούμενηΠροηγούμενη - Περιεχόμενα - ΕπόμενηΠεριεχόμενα Επόμενη

38 TitlePublicationShort Description «Towards Realizable Wireless Broadcast Systems»  TNET, submitted  MELECON2010, submited.Extended, client p.d.f agnostic, BS length aware mathematical analysis of no-bc broadcast problem.Introduction of cost-efficient scheduling algorithm.Deprecation of BDISKS model and State-of-art scheduling algorithms with respect to realizability. «Maximizing Adaptivity of dynamic Wireless Broadcast Systems»  Completed.Analysis on BS length minimization, which achieves minimal serving times. Analysis on the connection between adaptivity and BS length..Advanced Cost efficient, Adaptivity oriented algorithm «QoS on wireless broadcast Systems: Guaranteeing Serving time for real-time applications»  Completed.Analysis on BS construction aimed at guaranteeing serving time with X% certainty..Impact on the client set. 38 CACLAB Research Overview (part 2) ΠροηγούμενηΠροηγούμενη - Περιεχόμενα - ΕπόμενηΠεριεχόμενα Επόμενη

39 39 ΙV. Future Research ΠροηγούμενηΠροηγούμενη - Περιεχόμενα - ΕπόμενηΠεριεχόμενα Επόμενη

40 40 ΙV. New Research Objectives ΠροηγούμενηΠροηγούμενη - Περιεχόμενα - ΕπόμενηΠεριεχόμενα Επόμενη

41 41 Time Schedule R.S. YEAR 20102011 #1√√ #2√√ #3√√ #4√√ ΠροηγούμενηΠροηγούμενη - Περιεχόμενα - ΕπόμενηΠεριεχόμενα Επόμενη

42 42 Why Hybrids? ΠροηγούμενηΠροηγούμενη - Περιεχόμενα - ΕπόμενηΠεριεχόμενα Επόμενη

43 43 Questions?? ΠροηγούμενηΠροηγούμενη - Περιεχόμενα - ΕπόμενηΠεριεχόμενα Επόμενη