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1 Atomic Routing Games on Maximum Congestion Costas Busch Department of Computer Science Louisiana State University Collaborators: Rajgopal Kannan, LSU.

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Presentation on theme: "1 Atomic Routing Games on Maximum Congestion Costas Busch Department of Computer Science Louisiana State University Collaborators: Rajgopal Kannan, LSU."— Presentation transcript:

1 1 Atomic Routing Games on Maximum Congestion Costas Busch Department of Computer Science Louisiana State University Collaborators: Rajgopal Kannan, LSU Malik Magdon-Ismail, RPI

2 2 Introduction Price of Stability Price of Anarchy Outline of Talk Bicriteria Game

3 3 Network Routing Each player corresponds to a pair of source-destination Objective is to select paths with small cost

4 4 Main objective of each player is to minimize congestion: minimize maximum utilized edge

5 5 A player may selfishly choose an alternative path that minimizes congestion Congestion Games:

6 6 Player cost function for routing : Congestion of selected path Social cost function for routing : Largest player cost

7 We are interested in Nash Equilibriums where every player is locally optimal Metrics of equilibrium quality: Price of StabilityPrice of Anarchy is optimal coordinated routing with smallest social cost

8 8 Results: Price of Stability is 1 Price of Anarchy is Maximum allowed path length

9 9 Introduction Price of Stability Price of Anarchy Outline of Talk Bicriteria Game

10 10 We show: QoR games have Nash Equilibriums (we define a potential function) The price of stability is 1

11 11 number of players with cost Routing Vector

12 12 Routing Vectors are ordered lexicographically = = == < <= =

13 If player performs a greedy move transforming routing to then: 13 Lemma: Proof Idea: Show that the greedy move gives a lower order routing vector

14 14 Player Cost Before greedy move: After greedy move: Since player cost decreases:

15 15 Before greedy move player was counted here After greedy move player is counted here

16 16 > == No change Definite Decrease possible decrease possible increase or decrease Possible increase > END OF PROOF IDEA

17 17 Existence of Nash Equilibriums Greedy moves give lower order routings Eventually a local minimum for every player is reached which is a Nash Equilibrium

18 18 Price of Stability Lowest order routing : Is a Nash Equilibrium Achieves optimal social cost

19 19 Introduction Price of Stability Price of Anarchy Outline of Talk Bicriteria Game

20 20 We show for any restricted QoR game: Price of Anarchy =

21 Path of player 21 Consider an arbitrary Nash Equilibrium edge maximum congestion in path

22 must have an edge with congestion Optimal path of player 22 In optimal routing : Since otherwise:

23 23 In Nash Equilibrium social cost is:

24 24 Edges in optimal paths of

25 25

26 26 Edges in optimal paths of

27 27

28 28 In a similar way we can define:

29 29 We obtain sequences: There exist subsequence: Where: and

30 30 Maximum edge utilization Minimum edge utilization Maximum path length Known relations

31 31 Worst Case Scenario:

32 32 Introduction Price of Stability Price of Anarchy Outline of Talk Bicriteria Game

33 33 We consider Quality of Routing (QoR) congestion games where the paths are partitioned into routing classes: With service costs: Only paths in same routing class can cause congestion to each other

34 34 An example: We can have routing classes Each routing class contains paths with length in range Service cost: Each routing class uses a different wireless frequency channel

35 35 Player cost function for routing : Congestion of selected path Cost of respective routing class

36 36 Social cost function for routing : Largest player cost

37 37 Results: Price of Stability is 1 Price of Anarchy is

38 38 We consider restricted QoR games For any path : Path lengthService Cost of path

39 39 We show for any restricted QoR game: Price of Anarchy =

40 Path of player 40 Consider an arbitrary Nash Equilibrium edge maximum congestion in path

41 must have an edge with congestion Optimal path of player 41 In optimal routing : Since otherwise:

42 42 In Nash Equilibrium:

43 43 Edges in optimal paths of

44 44

45 45 Edges in optimal paths of

46 46

47 47 In a similar way we can define:

48 48 We obtain sequences: There exist subsequence: Where: and

49 49 Maximum edge utilization Minimum edge utilization Maximum path length Known relations

50 50 We have: By considering class service costs, we obtain:


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