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The Price of Routing Unsplittable Flow

The Price of Routing Unsplittable Flow. Yossi Azar Joint work with B. Awerbuch and A. Epstein. Outline. Game Theory and Selfish Routing Price of Anarchy Network Model – Previous Results Network Model – Our Results. Selfish Routing. Large networks

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The Price of Routing Unsplittable Flow

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  1. The Price of Routing Unsplittable Flow Yossi Azar Joint work with B. Awerbuch and A. Epstein

  2. Outline • Game Theory and Selfish Routing • Price of Anarchy • Network Model – Previous Results • Network Model – Our Results

  3. Selfish Routing • Large networks • Infeasible to maintain a central authority • Users are selfish • Each user tries to minimize its cost • Each user is aware of network conditions • Degradation of network performance

  4. Nash Equilibrium • Game Theory • Study and predict user behavior • Nash Equilibrium • Each agent minimizes its cost /maximizes its benefit • No agent has an incentive change its behavior

  5. Example-Prisoner’s Dilemma • Nash Equilibrium (c,c)

  6. Nash Equilibrium • Every game has randomized Nash equilibrium • In general a game may not have pure Nash equilibrium • No Deterministic Nash Equilibrium • Randomized Strategy pi,j= 0.5 is in N.E

  7. Parallel Links (Machines) Model • Two nodes • m parallel (related) links • n jobs • User cost (delay) is proportional to link load • Global cost (maximum delay) is the maximum link load

  8. Nash Equilibrium - Example • 2 identical links • 4 jobs with weights : 1,2,3,4 2 Not a Nash Equilibrium 1 4 3 Nash Equilibrium m1 m2

  9. Nash Equilibrium - Example 1 2 Optimal Solution 4 3 Also Nash Equilibrium m1 m2

  10. Price of Anarchy • Price of Anarchy (coordination ratio) : The worst possible ratio between: • Global cost in Nash Equilibrium and • Global cost in Optimum • Global cost: maximum/total user’s cost

  11. General Network Model • A directed Graph G=(V,E) • A load dependent latency function fe(.) foreachedge e • n users • Bandwidth request (si, ti, wi) for user i • Goal:route traffic to minimize total latency

  12. Example Latency function f(x)=x 1 2 1 2 1 t s 2 2 2 2 Latency=2+1+2=5 Latency=2+2+2+2=8 Total latency =Σe fe(le)·le= Σe le· le=6·2·2+3·1·1=27

  13. Example • Traffic rate r=1 • Nash total latency=1·0+1·1=1 f(x)=1 l=0 t s f(x)=x l=1

  14. Example • Traffic rate r=1 • Optimal total latency=1·1/2+1/2·1/2=3/4 • R=4/3 f(x)=1 l=1/2 t s f(x)=x l=1/2

  15. Braess’s Paradox • Traffic rate r=1 • Optimal cost=Nash cost=2(1/2·1+1/2·1/2)=3/2 fl(x)=1 l=1/2 f(x)=x l=1/2 v t s f(x)=1 l=1/2 f(x)=x l=1/2 w

  16. Braess’s Paradox • Traffic rate r=1 • Optimal cost did not change • Nash cost=1·1+0·1+1·1=2 • Adding edge negatively impact all agents fl(x)=1 l=0 f(x)=x l=1 v f(x)=0 l=1 t s f(x)=1 l=0 f(x)=x l=1 w

  17. Related Work-General Network Roughgarden and Tardos (FOCS 2000) • Assumption : each user controls a negligible fraction of the overall traffic • Results : • Linear latency functions - R=4/3 • Continuous nondecreasing functions-bicriteria results • Results hold also for nonnegligible splittable case (Roughgarden – SODA 2005) • Without negligibility assumption : nogeneral results

  18. Our Results • Unsplittable Flow, general demands • Linear Latency Functions • For weighted demands the price of anarchy is exactly 2.618 (pure and mixed) • For unweighted demands the price of anarchy is exactly 2.5. • Polynomial Latency Functions • The price of anarchy - at most O(2ddd+1) (pure and mixed) • The price of anarchy - at least Ω(dd/2)

  19. Remarks • Valid for congestion games • Approximate computation (i.e approximate Nash) has limited affect

  20. Routes in Nash Equilibrium • Pure strategies – user j selects single path QQj • Mixed strategies – user j selects a probability distribution {pQ,j} over all paths QQj

  21. Routes in Nash Equilibrium Definition ( Pure Nash equilibrium): System S of pure strategies is in Nash equilibrium iff for every j{1,...,n}and Q’  {Qj} : , where • Qj– path associated with request j

  22. Example Latency function f(x)=x Path Q1 1 USER 1 : W1=1 2 1 2 1 Path Q t s 2 2 2 2 CQ1,1 =2+1+2=5 CQ,1 =2+(1+1)+(1+1)+2=8

  23. Routes in Nash Equilibrium Definition : The expected cost C(S) of system S of mixed strategies is (i.e.the expected total latency incurred by S)

  24. Linear Latency Functions fe(x)=aex+be for each eE Theorem : For linear latency functions (pure strategies) and weighted demands R ≤ 2.618 Proof: • For simplicity we assume f(x)=x • Qj - the path of request j in system S • -set of requests that are assigned to edge e • - load of edge e • For optimal routes : Qj* , J*(e) , le*

  25. Weighted Sum of Nash Eq. • According to the definition of Nash equilibrium: • We multiply by wjand get • We sum for all j,and get

  26. Classification • Classifying according to edges indices J(e) and J*(e), yields • Using , we get

  27. Transformation • Using Cauchy Schwartz inequality, we obtain • Define and divide by • Then

  28. Unweighted Demands Theorem : For linear latency functions, pure strategies and unweighted demands R ≤ 2.5. Proof :

  29. Proof • As in the previous proof • Using , we get

  30. Proof • Applying properties • Then

  31. Linear Latency Functions Theorem : For linear latency functions and weighted demands R≥2.618. Proof: We consider a weighted network congestion game with four players

  32. Linear Latency Functions v Player 1 : (u,v, φ) Player 2 : (u,w, φ) Player 3 : (v,w, 1) Player 4 : (w,v, 1) 0 x u x x x 0 w OPT=NASH1=2φ2 + 2·12 = 2φ+4

  33. Linear Latency Functions v Player 1 : (u,v, φ) Player 2 : (u,w, φ) Player 3 : (v,w, 1) Player 4 : (w,v, 1) 0 x u x x x 0 w NASH2=2(φ+1)2 + 2·φ2 = 8 φ+6 R=φ+1=2.618

  34. Linear Latency Functions Theorem : For linear latency functions and unweighted demands R≥2.5. Proof: The same example as in the weighted case with unit demands

  35. Mixed Strategies Definition (Nash equilibrium): System S of mixed strategies is in Nash equilibrium iff for every j{1,...,n}and Q,Q’  {Qj}, with pQ,j>0 : cQ,j ≤ cQ’,j where • XQ,j – indicates whether request j is assigned to path Q • - load of edge e

  36. Mixed Strategies Theorem : For linear latency functions (mixed strategies) and weighted demands R ≤ 2.618. Proof : • Let {pQ,j}be the probability distribution of the system S. • The expected latency of user j for assigning his request to path Q in S is

  37. Step 1 • According to the definition of Nash equilibrium for , hence • We multiply by pQ,j·wjand get

  38. Step 2 • Sum over all paths and all users and classify according to the edges • Augment to • Obtain the same inequality as in the pure strategies case

  39. General Latency Functions • General functions-no bicriteria results • Polynomial Latency Functions • The price of anarchy - at most O(2ddd+1) (pure and mixed) • The price of anarchy - at least Ω(dd/2)

  40. Polynomial Latency Functions Theorem : For polynomials of degree d latency functions R = Ω(dd/2). Proof: We use the construction of Awerbuch et. al for the parallel links restricted assignment model.

  41. Example l=3 OPT Group 1 Group 2 Group 3 m0 m0 m0 m0 m0 m0 m1 m1 m1 m1 m1 m1 m2 m2 m2 m3 Group 3 NASH Group 2 Group 1 m0 m0 m0 m0 m0 m0 m1 m1 m1 m1 m1 m1 m2 m2 m2 m3

  42. The Construction • Total m=l! links each has a latency function f(x)=xd • l+1 type of links • For type k=0…l there are mk=T/k! links • l types of tasks • For type k=1…l there are k·mk jobs, each can be assigned to link from type k-1 or k • OPT assigns jobs of type k to links of type k-1 one job per link.

  43. System of Pure Strategies • System S of pure strategies • Jobs of type k are assigned to links of type k • k jobs per link • Lemma : The System S is in Nash Equilibrium.

  44. The Coordination Ratio

  45. Summary • We showed results for general networks with unsplittable traffic and general demands • For linear latency functions and weighted demands R=2.618 • For linear latency functions and unweighted demands R=2.5 • For Polynomial Latency functions of degree d , R=dӨ(d)

  46. Related Work-Machines Model • Main references • Koutsoupias and Papadimitriou (STACS 99) • Mavronicolas and Spirakis (STOC 2001) • Czumaj and Vocking (SODA 2002) • Awerbuch, Azar, Richter and Tsur (WAOA 2003) • …

  47. Related Work-Machines Model • Main results (global cost – maximum user’s cost) • For m identical links, identical jobs (pure) R=1 • For m identical links (pure) R=2 • For m identical links (mixed) • R- Price of Anarchy

  48. Mixed Strategies -Example • Machines model • (n=m) • Pure strategy – Assign job i to link i maximum cost=1 • Mixed strategy – assign jobs to links uniformly at random

  49. Related Work (Cont’) • Main results • For 2 related links R=1.618 • For m related links / restricted assignment (pure) : • For m related links / restricted assignment (mixed) : • R- Price of Anarchy

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