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©2002; Aberer,Datta,Hauswirth; LSIR-IC-EPFL. Laboratoire de systèmes d'informations répartis Updates: in a highly unreliable environment. Motivation: Maintaining.

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Presentation on theme: "©2002; Aberer,Datta,Hauswirth; LSIR-IC-EPFL. Laboratoire de systèmes d'informations répartis Updates: in a highly unreliable environment. Motivation: Maintaining."— Presentation transcript:

1 ©2002; Aberer,Datta,Hauswirth; LSIR-IC-EPFL. Laboratoire de systèmes d'informations répartis Updates: in a highly unreliable environment. Motivation: Maintaining consistent state in an unreliable environment. Update types: New data Update of existing data New node P-Grid reorganization

2 ©2002; Aberer,Datta,Hauswirth; LSIR-IC-EPFL. Laboratoire de systèmes d'informations répartis P-grid & updates

3 ©2002; Aberer,Datta,Hauswirth; LSIR-IC-EPFL. Laboratoire de systèmes d'informations répartis ‘Discovering’ new replicas 6

4 ©2002; Aberer,Datta,Hauswirth; LSIR-IC-EPFL. Laboratoire de systèmes d'informations répartis Epidemic algorithms: Taxonomy (classical) Epidemic algorithms Feedback or Blind Counter or Coin

5 ©2002; Aberer,Datta,Hauswirth; LSIR-IC-EPFL. Laboratoire de systèmes d'informations répartis Randomized rumor spreading A Unaware B Aware: Counter+Feedback C Aware: Counter D Aware: Stops Gossiping

6 ©2002; Aberer,Datta,Hauswirth; LSIR-IC-EPFL. Laboratoire de systèmes d'informations répartis Push&Pull in an unreliable environment. Push When a new update is received Pull If replica knows that it had been offline. If it receives some Push/Query after a long time, it may like to ‘synchronize’. At random time, if it feels ‘lonely’ for a long time.

7 ©2002; Aberer,Datta,Hauswirth; LSIR-IC-EPFL. Laboratoire de systèmes d'informations répartis Push

8 ©2002; Aberer,Datta,Hauswirth; LSIR-IC-EPFL. Laboratoire de systèmes d'informations répartis A B C D Duplicate messages

9 ©2002; Aberer,Datta,Hauswirth; LSIR-IC-EPFL. Laboratoire de systèmes d'informations répartis Analysis: Push phase - Notations

10 ©2002; Aberer,Datta,Hauswirth; LSIR-IC-EPFL. Laboratoire de systèmes d'informations répartis Analysis: Push phase Round 0

11 ©2002; Aberer,Datta,Hauswirth; LSIR-IC-EPFL. Laboratoire de systèmes d'informations répartis Analysis: Push phase Round 1

12 ©2002; Aberer,Datta,Hauswirth; LSIR-IC-EPFL. Laboratoire de systèmes d'informations répartis Analysis: Push phase Round 1

13 ©2002; Aberer,Datta,Hauswirth; LSIR-IC-EPFL. Laboratoire de systèmes d'informations répartis Analysis: Push phase Round 1

14 ©2002; Aberer,Datta,Hauswirth; LSIR-IC-EPFL. Laboratoire de systèmes d'informations répartis Analysis: Push phase Round t >= 2

15 ©2002; Aberer,Datta,Hauswirth; LSIR-IC-EPFL. Laboratoire de systèmes d'informations répartis Analysis: Push phase Round t >= 2 f aware (t)= f aware (t-1) + f Δ aware (t-1)

16 ©2002; Aberer,Datta,Hauswirth; LSIR-IC-EPFL. Laboratoire de systèmes d'informations répartis Analysis: Push phase Round t >= 2 ? ?

17 ©2002; Aberer,Datta,Hauswirth; LSIR-IC-EPFL. Laboratoire de systèmes d'informations répartis Analysis: Push phase Round t >= 2 ML(t) = U + R B L(t) L(t) : Normalized length of the partial list of replicas (to which update has been sent).

18 ©2002; Aberer,Datta,Hauswirth; LSIR-IC-EPFL. Laboratoire de systèmes d'informations répartis Analysis: Push phase Round t >= 2

19 ©2002; Aberer,Datta,Hauswirth; LSIR-IC-EPFL. Laboratoire de systèmes d'informations répartis Analysis: Pull phase Pull If replica knows that it had been offline. If it receives some Push/Query after a long time, it may like to ‘synchronize’. At random time, if it feels ‘lonely’ for a long time. Pull when Push is Over (Online replicas will have the update) Going on (It may get the update as a Push message)

20 ©2002; Aberer,Datta,Hauswirth; LSIR-IC-EPFL. Laboratoire de systèmes d'informations répartis Disquisition of the analytical results Varying initial online replicas

21 ©2002; Aberer,Datta,Hauswirth; LSIR-IC-EPFL. Laboratoire de systèmes d'informations répartis Disquisition of the analytical results Inferences: Without a significant online population, it is difficult to propagate the update. The message overhead is relatively independent of the online population (if it is significantly large) Varying initial online replicas

22 ©2002; Aberer,Datta,Hauswirth; LSIR-IC-EPFL. Laboratoire de systèmes d'informations répartis Disquisition of the analytical results Varying f r

23 ©2002; Aberer,Datta,Hauswirth; LSIR-IC-EPFL. Laboratoire de systèmes d'informations répartis Disquisition of the analytical results Inferences: No need to forward messages to too many replicas, since it increases message overhead without significant benifit. Varying f r

24 ©2002; Aberer,Datta,Hauswirth; LSIR-IC-EPFL. Laboratoire de systèmes d'informations répartis Disquisition of the analytical results Varying 

25 ©2002; Aberer,Datta,Hauswirth; LSIR-IC-EPFL. Laboratoire de systèmes d'informations répartis Disquisition of the analytical results Inferences: The strategy is very robust to replicas going offline without forwarding the update. Curiously message overhead decreases if some replicas ‘fail’ to forward the update ! Varying 

26 ©2002; Aberer,Datta,Hauswirth; LSIR-IC-EPFL. Laboratoire de systèmes d'informations répartis Disquisition of the analytical results Varying P F (t)

27 ©2002; Aberer,Datta,Hauswirth; LSIR-IC-EPFL. Laboratoire de systèmes d'informations répartis Disquisition of the analytical results Inferences: The best strategy is to reduce the probability of forwarding updates with increase in Push round. Proper tuning of PF(t) is essential, lest many replicas don’t recieve the update at all ! Varying P F (t)

28 ©2002; Aberer,Datta,Hauswirth; LSIR-IC-EPFL. Laboratoire de systèmes d'informations répartis Disquisition of the analytical results Parameter tuning and scalability

29 ©2002; Aberer,Datta,Hauswirth; LSIR-IC-EPFL. Laboratoire de systèmes d'informations répartis Parameter tuning and scalability Though the algorithm is devised for a scenario where the maximum number of replicas will be only in thousands, it scales well (provided parameters are tuned properly). Number of duplicate messages, t, L(t) may be used to tune the parameters. Feedforward for parameter tuning? (apart from using Feedback)

30 ©2002; Aberer,Datta,Hauswirth; LSIR-IC-EPFL. Laboratoire de systèmes d'informations répartis A B C D Duplicate message avoidance - advantages and disadvantages. E F G ?

31 ©2002; Aberer,Datta,Hauswirth; LSIR-IC-EPFL. Laboratoire de systèmes d'informations répartis Comparison with Gnutella like message flooding

32 ©2002; Aberer,Datta,Hauswirth; LSIR-IC-EPFL. Laboratoire de systèmes d'informations répartis Comparison with Gnutella like message flooding

33 ©2002; Aberer,Datta,Hauswirth; LSIR-IC-EPFL. Laboratoire de systèmes d'informations répartis Comparison with Gnutella like message flooding

34 ©2002; Aberer,Datta,Hauswirth; LSIR-IC-EPFL. Laboratoire de systèmes d'informations répartis Comparison with Gnutella like message flooding

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