Greening Backbone Networks

Greening Backbone Networks

30-minute presentation for the Green IT and Smart Grid course at Stony Brook University, on "Greening Backbone Networks" by Fisher et. al published at the first SIGCOMM green networking workshop in 2010.

Ed09e933a899fcae158439f11f66fed0?s=128

Emaad Manzoor

March 07, 2016
Tweet

Transcript

  1. Greening Backbone Networks Emaad Ahmed Manzoor CSE691: Green IT &

    Smart Grid March 2016 Will Fisher, Martin Suchara, Jennifer Rexford First Workshop on Green Networking, SIGCOMM ’10
  2. Backbone Networks

  3. https://en.wikipedia.org/wiki/File:Internet_map_1024.jpg

  4. https://commons.wikimedia.org/wiki/File:NSFNET-traffic-visualization-1991.jpg

  5. Aggregate Links (2-40 per-cable)

  6. http://www.klalaboratories.com/service/asset/network_cabling_3_lg.jpg

  7. https://www.reddit.com/r/mildlyinteresting/comments/3w14gh/this_giant_cable_stuffed_with_hundreds_of_wires/

  8. Router Line Cards

  9. https://farm4.staticflickr.com/3572/3567497578_aa942f35dd_b.jpg

  10. Not power-proportional https://farm4.staticflickr.com/3572/3567497578_aa942f35dd_b.jpg

  11. Power-proportional Router: Architectural Design and Experimental Evaluation, Bin Liu et.

    al. IWQoS 2014. Not power-proportional
  12. Cannot be put to “sleep”* Not power-proportional https://farm4.staticflickr.com/3572/3567497578_aa942f35dd_b.jpg

  13. Cannot be put to “sleep”* Not power-proportional *(2010) https://farm4.staticflickr.com/3572/3567497578_aa942f35dd_b.jpg

  14. Average link-utilisation in backbone networks is 30-40%.

  15. Can we power-down links (line cards) and adjust data flow

    based on traffic demand?
  16. Problem formalisation Naive solution Integer linear program Efficient heuristics Evaluation

    Future
  17. Problem formalisation Naive solution Integer linear program Efficient heuristics Evaluation

    Future
  18. Input

  19. G(V, E) s t w u v c(u,t) c(w,v) c(v,t)

    c(s,w) c(s,u) Topology Input
  20. G(V, E) s t w u v c(u,t) c(w,v) c(v,t)

    c(s,w) c(s,u) Topology D (s1, t1, h2) (s2, t2, h2) . . . Demands Input
  21. G(V, E) s t w u v c(u,t) c(w,v) c(v,t)

    c(s,w) c(s,u) Topology D (s1, t1, h2) (s2, t2, h2) . . . Demands Cables per link B Input
  22. Output

  23. s t w u v c(u,t) c(w,v) c(v,t) c(s,w) c(s,u)

    Flow of demand d on each link fd(s,u) fd(s,w) fd(w,v) fd(v,t) fd(u,t) fd(u, v) Output
  24. s t w u v c(u,t) c(w,v) c(v,t) c(s,w) c(s,u)

    Flow of demand d on each link fd(s,u) fd(s,w) fd(w,v) fd(v,t) fd(u,t) Total flow on link (u,v) f(u, v) = X d fd(u, v) fd(u, v) Output
  25. Problem formalisation Naive solution Integer linear program Efficient heuristics Evaluation

    Future
  26. min number of powered cables s.t. link loads ≤ capacities

    flow conservation carries all traffic demands
  27. min s.t. link loads ≤ capacities flow conservation carries all

    traffic demands X (u,v)2E f(u, v)
  28. min s.t. flow conservation carries all traffic demands X (u,v)2E

    f(u, v) X D fd(u, v)  c(u, v), 8(u, v) 2 E
  29. min s.t. carries all traffic demands X (u,v)2E f(u, v)

    X D fd(u, v)  c(u, v), 8(u, v) 2 E X v2V fd(u, v) = X w2V fd(w, u), 8d, 8u 6= sd, td
  30. min s.t. X (u,v)2E f(u, v) X D fd(u, v)

     c(u, v), 8(u, v) 2 E X v2V fd(sd, v) = X w2V fd(w, td) = hd, 8d X v2V fd(u, v) = X w2V fd(w, u), 8d, 8u 6= sd, td
  31. min number of powered cables s.t. link loads ≤ capacities

    flow conservation carries all traffic demands Models what if links are energy proportional? • Fractional linear program • Bounds on power savings
  32. Upper bound on energy savings f(u,v) = 6.3 Gbps (solution

    of FLP) c(u,v) = 10 Gbps (given topology) B = 10 (given cables per link) — Round down: 6 Gbps — Turn off: 4 cables
  33. Lower bound on energy savings f(u,v) = 6.3 Gbps (solution

    of FLP) c(u,v) = 10 Gbps (given topology) B = 10 (given cables per link) — Round up: 7 Gbps — Turn off: 3 cables
  34. Unfortunately, the lower bound solution is feasible but can be

    extremely suboptimal.
  35. (S, Ti, ✏) (Bi, Bi+1, ✏)

  36. Problem formalisation Naive solution Integer linear program Efficient heuristics Evaluation

    Future
  37. The bundle size B needs to be explicitly taken into

    account.
  38. min X (u,v)2E nuv Minimise the sum of the number

    of active cables on each link f(u, v)  nuv B c(u, v), 8(u, v) 2 E Number of active cables need to be sufficient for the demand
  39. min X (u,v)2E nuv Minimise the sum of the number

    of active cables on each link f(u, v)  nuv B c(u, v), 8(u, v) 2 E Number of active cables need to be sufficient for the demand NP-Complete — D2CIF
  40. Problem formalisation Naive solution Integer linear program Efficient heuristics Evaluation

    Future
  41. Fast Greedy Heuristic 1. Solve FLP - Obtain f(u,v), round

    up. 2. Find (u,v) with the most unused capacity - Remove one cable from the link. - Solve the FLP with the new capacities. Feasible? Remove a cable from the link (u,v).
  42. Fast Greedy Heuristic 1. Solve FLP - Obtain f(u,v), round

    up. 2. Find (u,v) with the most unused capacity - Remove one cable from the link. - Solve the FLP with the new capacities. Feasible? Remove a cable from the link (u,v). Not feasible? Mark link (u,v) as final.
  43. Fast Greedy Heuristic 1. Solve FLP - Obtain f(u,v), round

    up. 2. Find (u,v) with the most unused capacity - Remove one cable from the link. - Solve the FLP with the new capacities. Feasible? Remove a cable from the link (u,v). Not feasible? Mark link (u,v) as final. Repeat from 2, ignoring all final links
  44. Exhaustive Greedy Heuristic Remove the cable that - Retains solution

    feasibility. - Minimises the marginal penalty of each removal. Marginal penalty - Increases as excess traffic is rerouted along longer paths. - Measures as the FLP objective difference after cable removal.
  45. Bi-level Greedy Heuristic Exhaustive greedy heuristic with pairs of cables.

  46. Problem formalisation Naive solution Integer linear program Efficient heuristics Evaluation

    Future
  47. FGH EGH BGH

  48. Abilene Topology

  49. None
  50. None
  51. None
  52. 54% savings with B = 1 73% savings with B

    = 3
  53. None
  54. Problem formalisation Naive solution Integer linear program Efficient heuristics Evaluation

    Future
  55. https://tools.ietf.org/html/draft-zhang-greennet-01 Impact

  56. Dynamic demands Real-world traffic traces Approximation algorithms (?) Routing constraints

  57. .