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Optimum Broadcasting in Complete Weighted-Vertex Graphs

Optimum Broadcasting in Complete Weighted-Vertex Graphs. Hovhannes Harutyunyan Department of Computer Science and Software Engineering Concordia University, Montreal CANADA Shahin Kamali David Cheriton School of Computer Science University of Waterloo, Waterloo CANADA. Contents.

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Optimum Broadcasting in Complete Weighted-Vertex Graphs

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  1. Optimum Broadcasting in Complete Weighted-Vertex Graphs HovhannesHarutyunyan Department of Computer Science and Software Engineering Concordia University, Montreal CANADA ShahinKamali David Cheriton School of Computer Science University of Waterloo, Waterloo CANADA

  2. Contents • Problem definition • Weighted-Vertex Model • Previous Results • Optimum broadcasting in complete weighted-vertex graphs • Polynomial Algorithm • Proof of the correctness (scheme)

  3. Introduction • Information dissemination problems • Broadcasting (one-to-all communication) • Multicasting (one-to-many communication) • Gossiping (all-to-all communication) • Broadcasting: • A single message is sent from originator to all other members

  4. Introduction • Network is modelled by a graph G = ( V , E ) • Communication occurs in discrete rounds • The goal is to find a scheme which completes in minimum number of rounds • Communication model • defines what may happens in a round

  5. Introduction • Classical model of broadcasting • Each call involves one informed node and one of its uninformed neighbors • A node can participate in only one call per unit of time • In one unit of time, many calls can be performed in parallel • Finding optimum scheme is NP_complete

  6. Weighted-Vertex Model • The weights are non-negative integers • Applications in parallel machines, and Satellite-Terrestrial (ST) networks • The broadcasting completes when all nodes complete their internal delay • Each node needs to pass an internal delay, denoted by its weight, before passing the message to its neighbours

  7. Weighted-Vertex Model 0 5 A 5 7 8 6 9 B 2 C 3 D 0 E 1 8 8 10 10 9 9 10 11 10 F G H I J 3 1 4 4 2 12 11 15 12 13 13 14 K 5 L 0 18 14

  8. Existing Results • NP-hardness • Linear algorithm for trees (represent schemes with trees) • Approximation results: • O(b∗ log n +n1/2) • O(b∗ log n +n1/2) (b* the optimum broadcast time) • Better approximations exist for specific types of weighted graphs

  9. Broadcasting in Weighted-Vertex Complete Graphs • No symmetry ! • Find a polynomial algorithm for finding t optimum scheme

  10. Broadcasting in Weighted-Vertex Complete Graphs • Obvious answer: inform the neighbours with minimum weight b = 11 b = 7

  11. Algorithm Outline • Algorithm BroadGuess • Input: instance of problem + guess value for broadcast time br • Output: A broadcast scheme (tree) completes in time br OR BadGuess • Find the best scheme using a binary search approach • Upper bound for optimum broadcast time: bropt ≤ n + 1 + W • Call the algorithm with smaller or larger br Output is a scheme which completes within br Output is BadGuess

  12. BroadGuess • Start with a broadcast tree initialized by originator • Iteratively ‘Stick’ all other vertices to the tree • In each iteration • Stick the uninformed vertex with minimum weight (min) or maximum weight (max)

  13. BroadGuess br = 7 α = 1 • Let α be the soonest time to inform an uninformed node • If α + ω(min)+1+ω(max) ≤ br • Stick min to the tree to receive at time α • Otherwise • Find those indices where t(index)+ω(max) ≤ br • If none, return BadGuess • Stick max to inform it as late as possible     

  14. BroadGuess • α: • min: • max: br = 7 1 B (0) K (6) 2 B (0) J (4) 3 C (1) J (4) α + ω(min)+1+ω(max) = 1 + 0 + 1 + 6 = 8 > br 2 + 0 + 1 + 4 = 7 ≤ br 3 + 1 + 1 + 4 = 9 > br

  15. BroadGuess • α: • min: • max: br = 6 1 B (0) K (6) BadGuess α + ω(min)+1+ω(max) = 1 + 0 + 1 + 6 = 8 > br

  16. Proof of correctness • The output trees complete within candidate broadcast time • BadGuess No tree with broadcast time br(hard) • Express any optimum broadcast tree in a normal form which respects the candidate solution br • Show all trees in this form are ‘the same’ • Inform internal nodes in the same rounds • Show any subtree generated by the algorithm is ‘the same’ as normal trees • Inform internal nodes in the same rounds as normal forms

  17. Proof of correctness • A normal tree: • Non-lazy • Two internal nodes u,v: ω(u) ≤ ω(v) ⇔ t(u) ≤ t(v) • Internal node u, leaf x: ω(u) ≤ ω(x)

  18. Proof of correctness • A broadcast tree respects a candidate time br if for internal node u and leaf x, t(x) < t(u): t(u) + ω(x) > br AND t(u) + ω(u) + 1+ω(x) > br • Informing x can not be postponed with respect to br Respects br=7, but not br = 8

  19. Proof of correctness • A broadcast tree with time br  A normal broadcast tree which respects br • Proof: play with the links • Ex: Internal node u, leaf x: ω(u) ≤ ω(x) 

  20. Proof of correctness • Claim: BadGuess  No tree with broadcast time br • A broadcast tree with time br  A normal broadcast tree which respects br • In all normal trees which respect a candidate time br, internal nodes receive at the same time (proof omitted) • In the normal sub-tree generated by the algorithm, internal nodes receive at the same time as these trees (details omitted)

  21. Conclusion • There is a O(n2 log(n+W)) algorithm for optimum broadcasting in complete weighted-vertex graphs • Polynomial even if W=O(2^n)

  22. Thank you

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