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ElasticTree : Saving Energy in Data Center Networks

ElasticTree : Saving Energy in Data Center Networks . 許倫愷 2013/5/28. About the paper. Brandon Heller , Srini Seetharaman , Priya Mahadevan , Yiannis Yiakoumis , Puneet Sharma , Sujata Banerjee , Nick McKeown NSDI’10 ( USENIX conference on Networked systems design and implementation )

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ElasticTree : Saving Energy in Data Center Networks

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  1. ElasticTree: Saving Energy in Data Center Networks 許倫愷 2013/5/28

  2. About the paper • BrandonHeller, SriniSeetharaman, PriyaMahadevan, YiannisYiakoumis, PuneetSharma, SujataBanerjee, NickMcKeown • NSDI’10 (USENIX conference on Networked systems design and implementation) • Citation: 174 • 16 pages

  3. Outline • The big picture • Introduction • ElasticTree system • Analysis • Conclusion

  4. Outline • The big picture • Introduction • ElasticTree system • Analysis • Conclusion

  5. The motivation

  6. The motivation Very inefficient!! Desired

  7. Why wasting power • Provisioning for peak • Timevarying traffic demands • Low efficiency at low loads

  8. The goal of ElasticTree

  9. The approach… • Turn off unneeded links and switches

  10. The challenge • Performance • Fault tolerance • Scalability

  11. Outline • The big picture • Introduction • ElasticTree system • Analysis • Conclusion

  12. Introduction • What is ElasticTree: • ElasticTreeis a system for dynamically adapting the energy consumption of a data center network • What does it do: • Finding minimum-power network subsets across a range of traffic patterns • Trade-off: • energy efficiency, performance and robustness

  13. Introduction

  14. Data center network • (Traditional) 2N Tree: • One failure can cut the effective bisection bandwidth in half; two failures can disconnect servers

  15. Data center network • Fat tree: SIGCOMM 2008, A Scalable, Commodity Data Center Network Architecture

  16. Data center network • provision for peak workload • Traffic varies daily, weekly, monthly, and yearly.

  17. Energy Proportionality • The strategy: turn off the links and switches that we don’t need

  18. Outline • The big picture • Introduction • ElasticTree system • Analysis • Conclusion

  19. ElasticTree • ElasticTree is a system for dynamically adapting the energy consumption of a data center network

  20. ElasticTree • If 0.2 Gbps of traffic per host ,1 Gbps link…

  21. ElasticTree • 13/20 switches and 28/48 links stay active • ElasticTree reduces network power by 38% 0.8 0.4 0.2

  22. ElasticTree • The optimizer: find the minimum- power network subset which satisfies current traffic conditions

  23. Optimizer • As traffic conditions change, the optimizer continuously re-computes the optimal network subset • 3 approaches: • Formal Model , Greedy Bin-Packing , Topology-aware Heuristic

  24. Optimizer comparison

  25. Formal model • Finding the optimal flow assignment alone is an NP-complete problem for integer flows. • Derived from standard multi-commodity flow (MCF) problem • The model outputs a subset of the original topology, plus the routes taken by each flow to satisfy the traffic matrix • O(n^3.5+)

  26. Greedy Bin-Packing • Strategy: choose the leftmost one with sufficient capacity • O(n^2+) 1G link

  27. Greedy Bin-Packing 1G link

  28. Topo-aware Heuristic • 1. does not compute the set of flow routes • 2. assumes perfectly divisible flows • => pack every link to full utilization and reduce TCP bandwidth • => starter subset • Decoupling power optimization from routing : • => can be applied alongside any fat tree routing algorithm

  29. Topo-aware Heuristic • An edge switch doesn’t care which aggregation switches are active, but instead, how many are active

  30. Topo-aware Heuristic • Decoupling power optimization from routing

  31. Optimizer comparison

  32. Outline • The big picture • Introduction • ElasticTree system • Analysis • Conclusion

  33. How to test • K = 6, fat tree OpenFlow

  34. Analysis • Traffic pattern: • Near: servers communicate only with other servers through their edge switch • Far: servers communicate only with servers in other pods

  35. Analysis • Random demand: Individual aggregation/core switches turning on/off

  36. 70% to outside, 30% inside DCN Analysis Different traffic load

  37. Analysis: redundancy • If only the MST is on • => no redundancy => no fault tolerance

  38. Analysis: redundancy • +MST: additive cost, multiplicative benefit

  39. Analysis: latency Ethernet overheads (preamble, inter-frame spacing, and the CRC) cause the egress buffer to fill up Packets either get dropped or significantly delayed 0.33 0.5 0.25 Need safety margin!!

  40. Analysis: latency • Safety margin is the amount of capacity reserved at every link by the optimizer • Traffic overload is the amount each host sends and receives beyond the original traffic matrix Trade-off between Energy and Performance

  41. Outline • The big picture • Introduction • ElasticTree system • Analysis • Conclusion

  42. Summary

  43. Reference • The paper • The slide (by the author) • A youtube video (by the author, too) • http://www.youtube.com/watch?v=G2_D-CH4tQk

  44. Questions

  45. Thank you!

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