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Approaching Multicommodity Rent or Buy via Cost Sharing: A Steiner Forest Perspective

This work explores a cost-sharing algorithm for the Steiner forest problem and its application in the multicommodity rent or buy scenario. We introduce a constant approximation strategy for Steiner forests, examine expected costs, and utilize randomized techniques to optimize rent or buy decisions. The algorithm determines cost shares effectively, ensuring costs are shared equitably among demand pairs while maintaining a competitive approximation ratio. Related work highlights advancements in both Steiner forest algorithms and rent or buy strategies, paving the way for more efficient network designs.

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Approaching Multicommodity Rent or Buy via Cost Sharing: A Steiner Forest Perspective

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  1. Multicommodity Rent or Buy:Approximation Via Cost Sharing Martin Pál Joint work with Anupam Gupta Amit Kumar Tim Roughgarden

  2. Talk Outline • Cost sharing algorithm for Steiner forest: what do we want • Cost sharing for Steiner forest +Simple randomized analysis = Approximation for Rent or Buy • How does the cost sharing work

  3. Steiner Forest and Rent or Buy • Input: • Weighted graph G • Set of demand pairs D • Solution: • A set of paths, one for each (si,ti) pair

  4. Steiner Forest and Rent or Buy • Input: • Weighted graph G • Set of demand pairs D Steiner Forest: pay ce for each edge e we use, regardless of how many paths use it

  5. Steiner Forest and Rent or Buy • Input: • Weighted graph G • Set of demand pairs D Rent or Buy: pay ce per pair for renting, or buy for Mce

  6. Related Work • 2-approximation for Steiner Forest[Agarwal, Klein, Ravi 91] , [Goemans, Williamson 95] • Constant approximation for Rent or Buy[Kumar, Gupta, Roughgarden 02] • Spanning Tree + Randomization = Single Sink Rent or Buy[Gupta, Kumar, Roughgarden 03]

  7. Cost Sharing for Steiner Forest Cost sharing algorithm: approximation algorithm that given D, computes solution FD and set of cost shares (D,j) for jD

  8. Cost Sharing for Steiner Forest • Cost sharing algorithm: approximation algorithm that given D, computes solution FD and set of cost shares (D,j) for jD • (P1) Constant approximation: cost(FD)  St*(D) • (P2) Cost shares do not overpay: j (D,j)  St*(D) • (P3) Cost shares pay enough: let D’ = D  {si, ti} • dist(si, ti) in G/ FD  (D’,i)

  9. What does (P3) say

  10. What does (P3) say Solution for Solution for a b c ( , )   (a+c)

  11. The Rent or Buy Algorithm • Algorithm SimpleMRoB: • Mark each demand pair independently at random with prob. 1/M. Let S be the marked set. • Use a cost sharing algorithm to build a Steiner forest on the marked set S. • Rent shortest paths between all unmarked pairs.

  12. Analysis Claim: Expected cost of buying an optimal Steiner forest on S is at most OPT. Corollary: Expected cost of step 2    OPT Corollary: Expected sum of cost shares  OPT

  13. Analysis (2) Claim: Expected cost of step 3 is at most OPT. Let S’ = S – {i} E[cost share of i | S’] = 1/M M(S’+i, i) E[rental cost of i | S’] = (M-1)/M (dist(sj, tj) in G/FS’ ) From (P3): E[rental cost of i | S’]   E[cost share of i | S’] E[rental cost of i]   E[cost share of i]

  14. Analysis (3) There is a cost sharing algorithm with  = 6,  = 6. Theorem: SimpleMRoB is a 12-approximation algorithm.

  15. Existing Steiner forest algorithms • Each demand starts in a separate cluster • Active clusters grow at unit rate • When two clusters touch, they merge into one • Demands may get deactivated • A cluster is deactivated if it has no active demands • Inactive clusters do not grow • Keep adding enough edges so that all active demands in a component are connected

  16. Existing Steiner forest algorithms

  17. Existing Steiner forest algorithms

  18. Existing Steiner forest algorithms

  19. Existing Steiner forest algorithms

  20. Existing Steiner forest algorithms

  21. Existing Steiner forest algorithms

  22. How to define cost shares Need to pay for the growth of the clusters Active demands share equally the cost of growth of a cluster. a(u,) = 1 / (# of active demands in cluster with u) = 0 if u not active at time  cost share of u =  a(u,) d

  23. Does it work? • (P1) Constant approximation: cost(FD)  St*(D) • (P2) Cost shares do not overpay: j (D,j)  St*(D) • (P3) Cost shares pay enough: let D’ = D  {si, ti} • dist(si, ti) in G/ FD  (D’,i) • (P1) and (P2) easy to verify. How about (P3)?

  24. Does it work? 2 2 2+

  25. Does it work? 2 2 2+

  26. Does it work? 2 2 2+ cost share of = 2/n + 

  27. Does it work? 2 2 2+ cost share of = 2/n +  << 2 +  solution without

  28. Solution • Inflate the balls ! • Run the standard [AKR, GW] algorithm • Note the time Tj when each demand j deactivated • Run the algorithm again, except that now every demand j deactivated at time Tj for some  > 1 • Adopting proof from [GW]: buying cost at most 2OPT.

  29. Works on our example  = 2

  30. Works on our example

  31. Works on our example

  32. Works on our example

  33. How to prove (P3) Need to compare runs on D and D’ = D  {si, ti} Idea: 1. pick a {si, ti} path P in G/ FD 2. Show that (D’,i) accounts for a constant fraction of length(P)

  34. How to prove (P3) Need to compare runs on D and D’ = D  {si, ti} Idea: 1. pick a {si, ti} path P in G/ FD 2. Show that (D’,i) accounts for a constant fraction of length(P) Instead of (D’,i), use alone(i), the total time sior ti was alone in its cluster

  35. How to prove (P3) Need to compare runs on D and D’ = D  {si, ti} Idea: 1. pick a {si, ti} path P in G/ FD 2. Show that (D’,i) accounts for a constant fraction of length(P)

  36. How to prove (P3) Need to compare runs on D and D’ = D  {si, ti} Idea: 1. pick a {si, ti} path P in G/ FD 2. Show that (D’,i) accounts for a constant fraction of length(P)

  37. How to prove (P3) Need to compare runs on D and D’ = D  {si, ti} Idea: 1. pick a {si, ti} path P in G/ FD 2. Show that (D’,i) accounts for a constant fraction of length(P) (D’, ), = 2.5 alone( ) = 2.0

  38. Mapping of layers Lonely layer: generated by si or ti while the only active demand in its cluster Each non-lonely layer in the D’ run maps to  layers in the scaled D run. Idea: length(P) = #of lonely and non-lonely layers crossed in D’ run length(P)  #of layers it crosses in scaled D run Hence: for each non-lonely layer, -1 lonely layers crossed.

  39. Mapping not one to one Two non-lonely layers in D’ run can map to the same layer in scaled D run. D’ run scaled D run

  40. Not all layers of D run cross P A layer in D’ run that crosses P can map to a layer in scaled D run that does not cross P D’ run scaled D run si si The “waste” can be bounded.

  41. Open problems • Does SimpleMRoB work with unscaled GW? With an arbitrary Steiner forest subroutine? • Other cost sharing functions? (crossmonotonic..)

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